Alchemical free energy calculations (or Free Energy Perturbation -FEP-) are increasingly used in academia and industry to support ligand optimisation problems in the early stage of drug discovery. The domain of applicability of current alchemical methodologies has to date mainly been restricted to hit-to-lead and lead optimisation scenarios owing to limitations in computing cost, conformational sampling, and the accuracy of the potential energy functions used to compute protein-ligand energetics. There is continued interest in the development of more accurate potential energy functions to benchmark FEP workflows on diverse well curated diverse protein-ligand datasets, and for applications to blinded challenges or methodological studies. The calculation of hydration free energies has historically been an important stepping stone towards more accurate forcefields for protein-ligand binding free energy calculations. Blinded competitions such as SAMPL have also focused on hydration free energy calculations. Forcefield parameterization is a painstaking challenge that requires meticulous and laborious efforts to yield steady gains in accuracy. R ecent parameterization efforts from the Open Force Field, AMBER, CHARMM communities have involved multiple groups. Recent work has sought to simplify the parameterisation process by direct chemical perception of hierarchical parameter types. Nevertheless it can be difficult to identify what modifications to introduce to improve the accuracy of parameter sets. Ultimately fundamental limits in accuracy cannot be overcome due to an incomplete description of the physics of the process, for instance due to use of fixed-charge forcefields that neglect polarisation effects. Notably this realisation has prompted the development of post-processing methodologies based on quantum mechanical (QM) calculations to introduce correction terms for hydration and binding free energies computed by FEP methods using a classical force field. Data-driven machine-learning (ML) methods have witnessed a resurgence of interest in drug discovery in recent years. Impressive advances have been made in the area of machine learning of quantum chemical calculations, virtual screening, and free energies of hydration. Efforts such as DeepChem and MoleculeNet have popularised the use of ML methods for molecular property predictions. Recent efforts have made use of 3D convolutional neural networks or other graph convolutional neural networks to predict binding affinities from the spatial structure of protein-ligand systems. While impressive results have been demonstrated, the performance of ML methods is limited by the requirements of often substantial training sets, and a rapid decrease in accuracy when applying the models to molecules that are dissimilar to those that were included in the training set.