Users in many regions of the world are multilingual and they issue similar queries in different languages. Given a source language query, we propose query picking which involves finding equivalent target language queries in a large query log. Query picking treats translation as a search problem, and can serve as a translation method in the context of cross-language and multilingual search. Further, given that users usually issue queries when they think they can find relevant content, the success of query picking can serve as a strong indicator to the projected success of cross-language and multilingual search. In this paper we describe a system that performs query picking and we show that picked queries yield results that are statistically indistinguishable from a monolingual baseline. Further, using query picking to predict the effectiveness of cross-language results can have statistically significant effect on the success of multilingual search with improvements over a monolingual baseline. Multilingual merging methods that do not account for the success of query picking can often hurt retrieval effectiveness.