Inspired by the BOMB/MCPRO approach to molecular design, we introduce here the FEgrow open-source workflow for growing functional groups, chosen by the user, from a defined position on a core compound. To account for the multi-objective nature of molecular design, we output simple rule-of-five indicators of oral bioavailability, as well as flags for undesirable substructures and synthetic accessibility estimates. For the designed ligands, we enumerate 3D conformers of the added R-group, with options for additional flexibility if desired, within the context of the protein (discarding conformers with steric clashes). A common issue with generating docked poses is inaccuracy in the molecular mechanics force fields used to refine them, particularly for uncommon chemistries. To overcome this, we employ a hybrid machine learning / molecular mechanics (ML/MM) approach to optimisation, whereby the ligand is (optionally) described by the ANI neural network potential, and non-bonded interactions with the static protein are described by traditional force fields.