I see 3 options for you.

1. You should really consider upgrading your hardware, hdd -> ssd upgrade can multiply the speed of search by times.

2. Increase the speed of your search on the spot. You can refer to this question for various recommendations. The main idea of this method is optimize CPU load, but you will be limited by your HDD speed. The maximum speed multiplier is the number of your cores.

3. You can index your dataset. Because you're working with texts, you would need some full text search databases. Elasticsearch and Postgres are good options. This method requires you more disk space (but usually less than x2 space, depending on the data structure and the list of fields you want to index). This method will be infinitely faster (seconds). If you decide to use this method, select the analyzer configuration carefully to match what considered to be a single word for your task (here is an example for Elasticsearch)

Worth covering the topic from at two level: approach, and specific software to use.

Approach: Based on the way you describe the data, it looks that pre-indexing will provide significant help. Pre-indexing will perform one time scan of the data, and will build a a compact index that make it possible to perform quick searches and identify where specific terms showed in the repository.

Depending on the queries, it the index will reduce or completely eliminate having to search through the actual document, even for complex queries like 'find all documents where AAA and BBB appears together).

Specific Tool

The hardware that you describe is relatively basic. Running complex searches will benefit from large memory/multi-core hardware. There are excellent solutions out there - elastic search, solr and similar tools can do magic, given strong hardware to support them.

I believe you want to look into two options, depending on your skills, and the data (it will help sample of the data can be shared) by OP. * Build you own index, using light-weight database (sqlite, postgresql), OR * Use light-weight search engine.

For the second approach, using describe hardware, I would recommended looking into 'glimpse' (and the supporting agrep utility). Glimple provide a way to pre-index the data, which make searches extremely fast. I've used it on big data repository (few GB, but never TB).

See: https://github.com/gvelez17/glimpse

Clearly, not as modern and feature rich as Elastic Search, but much easier to setup. It is server-less. The main benefit for the use case described by OP is the ability to scan existing files, without having to load the documents into extra search engine repository.

Fs Crawler might help you in indexing the data into elasticsearch.After that normal elasticsearch queries can you be search engine.

Can you think about ingesting all this data to elasticsearch if they have a consistent data structure format ?

If yes, below are the quick steps:
1. Install filebeat on your local computer
2. Install elasticsearch and kibana as well.
3. Export the data by making filebeat send all the data to elasticsearch. 
4. Start searching it easily from Kibana.

To speed up your searches you need an inverted index. To be able to add new documents without the need to re-index all existing files the index should be incremental.

One of the first open source projects that introduced incremental indexing is Apache Lucense. It is still the most widely used indexing and search engine although other tools that extend its functionality are more popular nowadays. Elasiticsearch and Solr are both based on Lucense. But as long as you don't need a web frontend, support for analytical querying, filtering, grouping, support for indexing non-text files or an infrastrucutre for a cluster setup over multiple hosts, Lucene is still the best choice.

Apache Lucense is a Java library, but it ships with a fully-functional, commandline-based demo application. This basic demo should already provide all the functionality that you need.

With some Java knowledge it would also be easy to adapt the application to your needs. You will be suprised how simple the source code of the demo application is. If Java shouldn't be the language of your choice, its wrapper for Pyhton, PyLucene may also be an alternative. The indexing of the demo application is already reduced nearly to the minimum. By default no advanced functionlity is used like stemming or optimization for complex queries - features, you most likely will not need for your use-case but which would increase size of the index and indexing time.

I think if you cache the most recent searched medical data it might help performance wise instead of going through the whole 1TB you can use redis/memcached

There are a lot of answers already, I just wanted to add my two cents:

1. Having this much huge data(1 TB) with just 8 GB of memory will not be good enough for any approach, be it using the Lucene or Elasticsearch(internally uses Lucene) or some grep command if you want faster search, the reason being very simple all these systems hold the data in fastest memory to be able to serve faster and out of 8 GB(25% you should reserve for OS and another 25-50% at least for other application), you are left with very few GB of RAM.
2. Upgrading the SSD, increasing RAM on your system will help but it's quite cumbersome and again if you hit performance issues it will be difficult to do vertical scaling of your system.

Suggestion

1. I know you already mentioned that you want to do this on your system but as I said it wouldn't give any real benefit and you might end up wasting so much time(infra and code-wise(so many approaches as mentioned in various answers)), hence would suggest you do the top-down approach as mentioned in my another answer for determining the right capacity. It would help you to identify the correct capacity quickly of whatever approach you choose.

2. About the implementation wise, I would suggest doing it with Elasticsearch(ES), as it's very easy to set up and scale, you can even use the AWS Elasticsearch which is available in free-tier as well and later on quickly scale, although I am not a big fan of AWS ES, its saves a lot of time of setting up and you can quickly get started if you are much familiar of ES.

3. In order to make search faster, you can split the file into multiple fields(title,body,tags,author etc) and index only the important field, which would reduce the inverted index size and if you are looking only for exact string match(no partial or full-text search), then you can simply use the keyword field which is even faster to index and search.

4. I can go on about why Elasticsearch is good and how to optimize it, but that's not the crux and Bottomline is that any search will need a significant amount of memory, CPU, and disk and any one of becoming bottleneck would hamper your local system search and other application, hence advising you to really consider doing this on external system and Elasticsearch really stands out as its mean for distributed system and most popular open-source search system today.

You clearly need an index, as almost every answer has suggested. You could totally improve your hardware but since you have said that it is fixed, I won’t elaborate on that.

I have a few relevant pointers for you:

1. Index only the fields in which you want to find the search term rather than indexing the entire dataset;
2. Create multilevel index (i.e. index over index) so that your index searches are quicker. This will be especially relevant if your index grows to more than 8 GB;
3. I wanted to recommend caching of your searches as an alternative, but this will cause a new search to again take half a day. So preprocessing your data to build an index is clearly better than processing the data as the query comes.

Minor Update:

A lot of answers here are suggesting you to put the data in Cloud. I'd highly recommend, even for anonymized medical data, that you confirm with the source (unless you scraped the data from the web) that it is ok to do.