We have developed an efficient algorithm for comparing two chemical compounds, where the chemical structure is treated as a 2D graph consisting of atoms as vertices and covalent bonds as edges. Based on the concept of functional groups in chemistry, 68 atom types (vertex types) are defined for carbon, nitrogen, oxygen, and other atomic species with different environments, which has enabledd etectiono f biochemicallym eaningfulf eatures.M aximalc ommons ubgraphs of two graphs can be found by searching for maximal cliques in the association graph, and we have introducedh euristicst o acceleratet he clique finding. Our heuristicp rocedurei s controlled by some adjustablep arameters. Herew e appliedo ur proceduret o the latest KEGG/LIGANDd atabase with different sets of parameters, and demonstrated the correlation of parameters in our algorithm with the distribution of similarity scores and/or the execution time. Finally, we showed the effectiveness of our heuristics for compound pairs along metabolic pathways.