Most people think of search and immediately think of large data sets, but the problems that plague smaller datasets are equally interesting. It's less about performance and more about relevance. For e.g. searching across multiple fields for a compound query like "taylor swift style", requires breaking the query into segments (taylor swift | style) before searching for the appropriate fields. There are also a class of problems that traditional search engines that rely on BM25 or TF-IDF for ranking cannot reliably solve (e.g. searching on small texts like titles) where you have to consider distance between matching words (which TF-IDF and BM25 miss). Lastly, there is also personalization which is almost always left as an exercise to the reader :)