In peer-to-peer (P2P) networks, computers with equal rights form a logical (overlay) network in order to provide a common service that lies beyond the ca- pacity of every single participant. Efficient similarity search is generally recognized as a frontier in research about P2P systems. One way to address it is using data source selection based approaches where peers summa- rize the data they contribute to the network, generat- ing typically one summary per peer. When process- ing queries, these summaries are used to choose the peers (data sources) that are most likely to contribute to the query result. Only those data sources are con- tacted. There are two main contributions of this paper. We extend earlier work, adding a data source selec- tion method for high-dimensional vector data, compar- ing different peer ranking schemes. More importantly, we present a method that uses progressive stepwise data exchange between peers to better each peer?s summary and therefore improve the system?s performance.