Simple drug-likeness rules, multivariate methods for DMPK properties (solubility, permeability, clearance, etc.) and safety, as well as docking scores are typically included in reward scores as filters. However, several thousands of compounds will inevitably still pass those filters. This is related to the common lack of sufficient high-quality data, and the fact that we still often struggle with making predictions to the required accuracy. Prediction of biological activity is an extremely hard problem since many phenomena involved are difficult to quantify precisely. Standard docking scores are most often not sufficient. Although, at times, methods such as Free Energy Perturbation (FEP) can improve the scoring accuracy for small perturbations of one structure into another, but not for major structural changes. The use of FEP combined with active learning is gaining traction and is showing promise. Nonetheless, when the output contains many structurally diverse molecules, as frequently is the case for expressive SMILES-based generative methods, current methods' accuracies are not sufficient to filter down many compounds to a selected few. Despite the increasing prevalence of physics-based models in generative modelling, bioaffinity prediction remains very challenging.