We conclude that transformer models can be a powerful tool for hit expansion in drug design. They can go beyond MMP in generating reasonable context-dependent modification of bioactive molecules and demonstrate the ability to predict the most active known molecules, exactly or up to minor modification, in retrospective tests on public datasets. A common informatictheory-based score of the quality of predictions, namely perplexity, correlates with chemical scores, and can be used instead of the latter, that are more difficult to formalize and, if formalized, are often non-differentiable (such as the number of novel molecules, scaffolds, R-group replacements, etc.). Finally, we demonstrate that the entrance barrier for this method of hit expansion is low, as proven by its successful implementation during a summer internship of the first author of this paper, an undergraduate student without prior professional experience in drug design (E.P.T.).