Binding affinity is an important metric in biology and medicinal chemistry that measures the strength of a reversible association between biological macromolecules, such as proteins or DNA, and small-molecule ligands, such as drugs. It is, therefore, a central concept of rational drug design, where the potential efficacy of a drug is measured by its binding affinity to a known biological target implicated in a pathology the drug should treat . Traditionally, simulation-based molecular docking techniques, such as scoring functions, overfit to use-cases that adhere to rigid modelling assumptions, do not fully represent protein flexibility, and do not directly account for solvent effects . Non-parametric machine learning has been proposed as an alternative to infer complex binding effects that are difficult to explicitly represent directly from experimental data . Therefore, a wide array of neural network architectures combined with a multitude of scoring functions have been proposed . approaches that make use of engineered features and precomputed molecular descriptors such as the ECFP-inspired extended connectivity interaction features (ECIF) .