To better simulate the original field of transformer models, we train them on SELFIES of pairs of similar bioactive molecules. Such datasets of pairs can be generated from a large corpus of bioactive molecules (specifically, ChEMBL in this work). A transformer model trained on such pairs of molecules should learn chemically-meaningful rules for modifications in a molecule that have a high chance of resulting in another biologically active molecule. Then, known structures of active compounds can be inputted into a trained model to generate new molecules, and therefore, new ideas for hit expansion. The conceptual similarity of this approach to the initial use of transformers allowed us to hope that the goal could be achieved in a relatively quick and straightforward way. The availability of software building blocks and reference transformer implementations from other researchers made it possible to implement and benchmark our implementation during the course of a summer internship of the first author (E.P.T.).