The success of Web search is often limited by a variety of factors. Typical queries are vague and imprecise. At the same time, the Web is a dynamic and unmoderated collection and term-based indexing techniques often fail to produce high quality indices. As a result there is often a significant gap between the query-space and the document-space and the terms used to query a document may vary from those that have been used to index it. In this paper we describe the I-SPY collaborative search engine. It uses information gleaned from the query-space as a supplementary index with which to retrieve documents. We show how prior user selection histories can be used to estimate the relevance of documents to queries, and how this can be used to re-rank documents during search. The results of a live-user trial are presented to demonstrate the potential benefits of I-SPY when compared to more traditional search techniques. KEYWORDS Web intelligence, search, context, relevance, personalization.