We present a fast algorithm for finding large common sub-graphs, which can be exploited for detecting structural and functional relationships between biological macromolecules. Many fast algorithms exist for finding a single maximum common subgraph. We show with an example that this gives limited information, motivating the less studied problem of finding many large common subgraphs covering different areas. As the latter is also hard, we give heuristics that improve performance by several orders of magnitude. As a case study, we validate our findings experimentally on protein graphs with thousands of atoms.