Modern web search engines are making increasing use of signals other than mere textual statistics. While documents used to be matched to keyword queries based on term counting alone, modern information retrieval systems incorporate and learn from a large number of features pertaining to the query, user, documents, entities, sessions, etc. In particular, a document ranking generated by a web search engine involves combining signals from rich representations of users (including their location, browser, device, profile, history, etc.), semantics (ranging from simple spell-checking to recognizing entities), popularity, social networking, and more. All of these features need to be computed at an increasingly large scale and call for Big Data storage and analytics methods. In this talk I will give some examples of current IR research being done at the University of Amsterdam, leaning heavily on MapReduce and related programming paradigms.