Community search aims to find out communities containing a given set of nodes and get personalized community information quickly. Since traditional community search algorithms can hardly meet the needs under complex conditions, a new problem called conditional community search is proposed. Solving the problem helps to analyze social networks intelligently and provides users with better community results under complex search conditions. First, based on Boolean expressions, the formal definition of conditional community search problem is given, which can effectively express the requirement that a given node cannot exist in the community and at least one of the given nodes occurs in the community. Then, a general framework is proposed to solve the problem of conditional community search, including simplifying search conditions, conducting multiple singleconditional community searches according to simplified search conditions, and combining the results of singleconditional community searches. At the same time, a community search plus filtering method and a node weighting based method are proposed to carry out the singleconditional community search. Finally, extensive experimental results conducted on real-world datasets show the correctness andeffectiveness of the proposed methods.