Sort of! Your approach of "searching filters" is certainly a shortcut into larger query understanding, and for your use-case, depending on the size and variety

Sort of! Your approach of "searching filters" is certainly a shortcut into larger query understanding, and for your use-case, depending on the size and variety of your catalog, it may be totally sufficient. You're actually light-years ahead of most search engines with this strategy! Awesome work. Where I might go next with this would be to investigate the search results and evaluate the quality of those results, and then improve that over time by increasing your query understanding of what the users are asking for. One question to ask yourself is "What is the purpose of search?" My answer is "to find what I'm looking for as fast as possible." Remember, time is money, and if it takes customers longer to find what they're looking for on your site versus another e-commerce site (or worse yet, an actual physical store), then they'll leave and go to the other site. Speed and low effort is key! (Note: If you are happy with conversion rates, then you can stop here -- don't go down rabbit holes you don't need to go down. There are other cool projects to work on, too!) So, given this idea of making this as efficient for customers as possible, what's the least amount of effort (and therefore the fastest) way for a customer to purchase something? The answer, of course, is to find it on the first try! (This reminds me of that scene in Happy Gilmore where Happy hits a hole in one, and he says to his coach, "I should just do that every time.") Imagine I search for `blue shirt`, and the shirt I wind up buying is the 12th item on the list. From a quantitative standpoint, the conversion rate is 100%. I searched for something, and then I bought it! But qualitatively, the user experience is sub-par: you showed me eleven things I wasn't interested in before the 12th thing that I was interested in! This is cognitive load, and every time I have to look at the _next_ item in the list (or worse, go to the second -- or god forbid, the third! -- page), that's a chance that I'll give up and leave, or conduct another search and be equally frustrated. The goal here, then, is to try and get the right item in the first slot 100% of the time. This is a bit of a holy grail, but it's a good target to aim for. If your users' queries are simple and straightforward and your catalog is sufficiently small and focused, simply doing ANDs against filters might be good enough, but if you have a huge variety, then you may wind up having the "right" blue shirt be pretty far down the list. How do you fix this? Well, first you need data. Track conversion rates, track which index position the conversion, and look at keywords that people are looking for. Also look at search queries that result in zero hits! Consider typo correction and synonyms to ensure maximum coverage of those queries. If you start identifying keywords that folks are searching for but finding zero or low conversion, then you have some interesting information! Either start carrying products that match those terms, or start tagging relevant products with those terms to increase conversion rates and reduce zero-hit results. Then you possibly need some sort of popularity metric (sales volume over a period of time is a good one) or some sort of personalization engine (looks like you have something, but I generally don't recommend it for folks just starting out). That way the most popular "blue shirt" will be first -- the odds are fantastic that your customer's preferences are in line with the majority -- and this should boost the conversion index to be closer to that first slot. Also, as sort of a tangent, IMO it's also better to say "Hey, we don't have that," than to return bad stuff. Again, imagine that I search for `blue shirt` and you don't actually have any blue shirts. In that case, you may drop `blue` and return `shirts`, or maybe you drop `shirts` and return `blue`, and now I see red shirts and blue pants. It's not clear to me as a user that there are no blue shirts, so now I waste time looking through potentially pages of results! Other things to consider, which I think you already discovered: 1) Autocomplete is a powerful way to search. Naively, you autocomplete the `products` table, but as you discovered, it's almost better to autocomplete search queries (or filters). This probably means you need to roll your own autocomplete system, tracking the most popular search terms/filters over time, and dropping the less popular searches from the list. I have a lot of thoughts about this, but it's not insanely complicated, so I'll stop here. 2) Sorting/Filtering is really powerful, but it's also really easy to screw up. In general, my rule of thumb is to sort by things that are either objectively useful (filters) or by something where I control the definition. For example, sorting by "price" is something that's highly requested, but sorting by price often returns unintuitive results. Consider Amazon and searching for "dslr camera" and then sort by "Price - Low to High." What you really want to see is cameras -- but what you see at the top of the list (below the ads) isn't cameras, it's camera accessories. Lens caps, straps, batteries. That's not what you meant. Instead, we would want to sort by "Best Deal", which means the top item might not be the objective lowest price, but it's the lowest priced camera, which is what you're asking for. (And your query understanding model needs to recognize that the entity is "camera" in order for this to work!) If you promise "Price - Low to High" and the first item (an actual camera) is $300 and the second item (battery) is $25, people might feel like either the filter is broken or that you don't know what you're doing. If you use subjective language like "Best Deals" or "Trending," you can play around with the results and build trust more easily. 3) Temporality is introducing the concept of time. Over time, preferences chance. Certain search terms fade from use at certain times of year. People stop searching for "sandals" in the fall and start searching for "boots," and so if you type in "brown shoes" in November, you probably want to ensure that boots are at the top, and not Birkenstocks. This is a pretty fun problem, but you also have to be careful because maybe they did want sandals, and you don't want to mess that up. I'm just rambling at this point. Very passionate about this topic. Good luck with your system!