The results illustrate the power and flexibility of molecular set representation learning. While the other methods rely on DFT calculations, pretrained large language models, or a custom molecular fingerprint in the case of DRFP, MSR2-RXN uses the known simple and well-established ECFP embedding to represent the molecules participating in the reaction.