Large-scale distributed data management with P2P systems requires the existence of similarity operators for queries as we cannot assume that all users will agree on exactly the same schema and value representations and data quality problems due to spelling errors and typos. In this paper, we present an approach for efficient processing of similarity selections and joins in a structured overlay. We show that there are several possible strategies exploiting DHT features to a different extent (i.e., key organization, routing, multicasting) and thus the choice of the best operator implementation in a given situation (selectivity, data distribution, load) should be based on cost information allowing the system to estimate the computation and communication costs of query execution plans. Hence, we present a cost model for similarity operations on structured data in a DHT and demonstrate the efficiency of our proposal by experimental results from a large-scale PlanetLab deployment.