Reaction rebalancing with SynRBL can provide much larger and more diverse sets of stoichiometrically balanced reactions as a basis for a wide variety of data-driven tasks in cheminformatics. In particular, we expect that better atom-atom-maps can be obtained from such balanced data since the mappers are freed from the need to solve the reaction balancing problem simultaneously. We expect beneficial effects also on learning approaches, e.g. in forward prediction, retrosynthesis planning, and, notably, the elucidation of reaction mechanisms. Finally, representations of reaction mechanisms in the form of graph transformation rules could be employed as an orthogonal validation strategy, particularly on data sources where named reactions are annotated in the metadata. As described in the main text, the MCS problem was solved in several different versions ("configurations"), none of which is guaranteed to always identify the chemically correct common subgraph. We benchmarked the different variants and found that they are at least in part complementary. As depicted in Fig. , spanning panels A through F, three distinct cases of MCS were identified, where configurations 1 to 4