Although answering list questions is not a new research area, answering them automatically still remains a challenge. The median F-score of systems that participated in TREC 2007 Question Answering track is still very low (0.085) while 74% of the questions had a median F-score of 0. In this paper, we propose a novel approach to answering list questions. This approach is based on the hypothesis that answer instances of a list question co-occur in the documents and sentences related to the topic of the question. We use a clustering method to group the candidate answers that co-occur more often. To pinpoint the right cluster, we use the target and the question keywords as spies to return the cluster that contains these keywords.