The FEgrow software package is described in detail elsewhere. Briefly, FEgrow aims to grow a ligand within a protein binding pocket, starting from a provided receptor structure, ligand core and growth vector (Figure ). Libraries comprising 2000 linkers and around 500 R-groups, are provided, or users can supply their own. Merging is achieved using the RDKit package, which also generates an ensemble of ligand conformations via the ETKDG algorithm, with the atoms of the core strongly restrained to the input structure. That is, the default behaviour is to allow flexibility only in the regions of the grown linkers and R-groups. The ensemble of ligand structures is filtered to remove any that clash with the protein, and the remaining conformers are structurally optimised in the context of a rigid protein binding pocket using the OpenMM software. During energy minimisation, the protein is treated using the AMBER FF14SB force field, while intramolecular energetics of the ligand are described, where possible, using the ANI-2x machine learning potential. Non-bonded interactions between the protein and ligand are described using a mechanical embedding scheme, that is, they use electrostatics and Lennard-Jones terms described by either the Open Force Field 'Sage' 41 or GAFF2 general force fields. The goal of this hybrid machine learning / molecular mechanics approach is to correct for known deficiencies in potential energy surfaces of classical force fields, while ensuring that optimisations are significantly faster than using full QM/MM. The lowest energy structures are then output for scoring. In the first iteration of FEgrow, we used the gnina convolutional neural network (CNN), which has been jointly trained on binding pose and affinity prediction. We showed that the gnina 'CNNaffinity' scores (predicted pK) correlated reasonably well with experiment for ten series of congeneric inhibitors built using FEgrow. Here, we add further options for scoring molecules based on protein-ligand interaction profile (PLIP), molecular properties, or a combination thereof.