With the design of FEgrow, we hope to overcome some of the current limitations of de novo drug design discussed in the Introduction. Some of these limitations are addressed in the current study, and some will be addressed in future aided by ongoing advances in molec-ular modelling and machine learning. For example, we tackle the question of binding pose optimisation by using a fast and accurate machine learning potential (ANI-2x 17 ) to describe the ligand energetics in a mechanical embedding scheme. However, with the flexibility of the FEgrow interface with OpenMM, 18 new models could be substituted in, and these are now approaching sufficient speed and accuracy (including for long-ranged interactions) such that the entire protein-ligand complex could be described using a single, consistent machine learning potential. In this study, we made the approximation that the protein binding pocket is rigid and used a single receptor structure for design. However, now that ligand building and scoring is fully automated, future studies could use, for example, ensembles of receptor structures, which may be beneficial in cases where the pocket is more flexible.