Through this brief case study, we demonstrate that the transformers developed here have immediate value for lead optimization by densely sampling the near chemical space around a given source, and even without any explicit steering toward desirable properties (for example, via reinforcement learning ), they can suggest molecules with improved predicted properties. The extensiveness of the this search is controlled by the beam size, but even using a small beam of 1, 000 ideas, we were able to rapidly generate reasonable suggestions for further prioritization.