As for transformer models in generative chemistry, they have demonstrated noticeable success, but for purposes different than in this work. For example, transformers were used to generate structures of molecules from their text description in English or even from the primary amino acid sequence of a protein they should bind to. Fragment replacement with optimization of logP, PSA and other easily computable properties was reported. New molecules with desired values of logP, TPSA, QED (and SAS in the second case) were generated from a seed molecule 6 or a scaffold. A combination of a Restricted Boltzmann Machine (RBM) with two transformer models was built, with the hope to replace an RBM with a Quantum Boltzmann Machine in the future, and thereby enable quantum computing in drug design. A latent space representation of organic molecules was constructed with a transformer model and contrastive learning approach. A transformer model, pre-trained on ChEMBL to generate valid SMILES, was trained on molecules active against a given protein target by transfer learning and reinforcement learning, and used to generate novel molecules. Note that in all these papers generated molecules were validated only based on their easily computable properties, such as logP and QED (typically, by comparisons of distributions of these properties with those for the input or reference sets); the most advanced comparison, as far as we know, used docking scores and similarity to known bioactive molecules. Also, the cited papers address, in essence, the problem of de novo compound design. For example, in Ref. 8, the model was transferlearned on 4702 known molecules active against the protein target, much more than what we usually have for hit expansion, then 50k molecules were generated by the model and prioritized docking. In this work, we follow the best practices in the field, validating generated molecules by their easily computed properties and a comparison to known active molecules. We apply transformer models for the first time, as far as we know, to the task of hit expansion.