A novel method for creating collection summaries is developed, and a fully decentralized peer-selection algorithm is described. This algorithm finds the most promising peers for answering a given query. Specifically, peers publish per-term synopses of their documents. The synopses of a peer for a given term are divided into score intervals and for each interval, a KMV (K Minimal Values) synopsis of its documents is created. The synopses are used to effectively rank peers by their relevance to a multi-term query The proposed approach is verified by experiments on a large real-world dataset. In particular, two collections were created from this dataset, each with a different number of peers. Compared to the state-of-the-art approaches, the proposed method is effective and efficient even when documents are randomly distributed among peers. © 2010 ACM.