Memory queries denote queries where the user is trying to recall from his/her past personal experiences. Neither Web search nor structured queries can effectively answer this type of queries, even when supported by Human Computation so- lutions. In this paper, we propose a new approach to answer memory queries that we call Transactive Search: The user- requested memory is reconstructed from a group of people by exchanging pieces of personal memories in order to reassem- ble the overall memory, which is stored in a distributed fash- ion among members of the group. We experimentally com- pare our proposed approach against a set of advanced search techniques including the use of Machine Learning methods over the Web of Data, online Social Networks, and Human Computation techniques. Experimental results show that Transactive Search significantly outperforms the effective- ness of existing search approaches for memory queries.