We have introduced here FEgrow, an open-source molecular builder and free energy preparation workflow. Taking as input a receptor and ligand core structure, FEgrow aims to build a user-defined library of chemical functional groups of the sort that would typically be used to explore structure-activity relationships with free energy calculations. By growing from a fixed core, and making use of hybrid ML/MM structural optimisation, we aim to maximise the use of binding mode information from structural biology sources and obtain reliable coordinates for the added R-groups. In this respect, the ANI neural network potential (within the ML/MM approximation) has already been shown to be capable of predicting protein-ligand binding poses in agreement with electron density distributions determined by x-ray crystallography, and updated releases or semi-empiricial methods can readily be included in future versions of FEgrow. Ligand designs are evaluated for simple molecular properties, and their binding affinity predicted using the gnina CNN scoring function.