Here, we propose shape and electrostatic potential matching as a strategy to bias generative models to propose compounds with different fragments (that are likely bioisosteres) of known lead compounds. The tool is designed to generate novel molecules with optimized properties. One example usage is scaffold hopping. Here it should be noted that there are many other scaffold hopping tools available ranging from CAVEAT, which is one of the early 3D database searching programs, to the more recent BROOD. In the context of generative methods, Langevin et al. recently described a new RNN-based algorithm, named SAMOA (Scaffold Constrained Molecular Generation), to perform scaffold-constrained molecular design. Generative methods benefit from the associated reinforcement learning methods, allowing multiobjective molecular design optimization, while only exploring the relevant chemical space.