Users of current search systems actively interact with the system to complete their search task. This can encompass formulating and reformulating a series queries expressing evolving of different information needs. We believe that the next generation of search systems will see a shift towards proactive understanding of user intent based on analysis of user activities. Such a proactive search system could start recommending documents that are likely to help users accomplish their tasks without requiring them to explicitly submit queries to the system. We propose a framework to evaluate such a search system. The key idea behind our proposed metric is to aggregate a correlation measure over a search session between the expected outcome, which in this case refers to the list of documents retrieved with a true user query, and the predicted outcome, which refers to the list of documents recommended by a proactive search system. Experiments on the AOL query log data show that the ranking of two sample proactive IR systems induced by our metric conforms to the expected ranking between these systems.