As adaptive peer network systems becoming an increasingly important development in Web search technology, in this research, an alternative model for peer based Web search is introduced to address the scale problem of centralized search engines. Queries are first matched against the local engine, and then routed to neighbor peers to obtain more results. Initially the network has a random topology (like Gnutella) and queries are routed randomly as in the flood model. However, the protocol includes a learning algorithm by which each peer uses the results of its interactions with its neighbors to refine a model of the other peers. This model is used to dynamically route queries according to the predicted match with other peers’ knowledge. The network topology is thus modified on the fly based on learned contexts and current information needs.