In comparison to traditional graph search, containment search has its own indexing characteristics that have not yet been examined. We propose a scalable contrast subgraph-based indexing model, called csgIndex. Using a redundancy-aware feature selection process, csgIndex can sort out a set of significant and distinctive contrast subgraphs and maximize its indexing capability. Taking this solution as a base indexing model, we further extend it to accommodate hierarchical indexing methodologies and apply data space clustering and sampling techniques to reduce the index construction time. Experimental results on real test data show that csgIndex achieves near-optimal pruning power on various containment search workloads, and confirms its obvious advantage over indices built for traditional graph search in this new scenario.