The PDBbind dataset is widely used for training affinity prediction models. It (v.2020) contains approximately 20000 3D protein-ligand X-ray structures of known binding affinity measured as the inhibition constant (Ki), dissociation constant (Kd), or half-maximum inhibition concentrations (IC50). The set is further split into a refined set of around 5000 high-quality X-ray structures and the most reliable affinity data (Ki and Kd only) and a core set of 290 complexes from 58 different proteins with five different ligands of various affinities. Either with or without data augmentation (inactive compounds, docking pose augmentation) it represents the best public data source to train 3D models. However, various concerns regarding ML/DL affinity prediction have arisen and the composition and completeness of the training dataset is at the forefront. The major problem relates to the sparsity of the protein-ligand training matrix. The sparsity of the training dataset is the fraction of the ligand-protein matrix having a missing experimental binding affinity. A dataset with a large representation of targets can cover more of the available protein space but the sparsity of the data is high, as usually few of the ligands are tested against the biological space and the dataset becomes heavily biased. Simple experiments have shown that 3D ML/DL affinity models based only on protein description or only on ligand description (omitting interactions) can show modest to good performance when trained and tested on PDBbind data. In other words, there are biases in the data that are relatively easy to uncover, the bioactivity is likely clustered for each protein and chemical series due to the lack of sufficient protein-ligand complexes per target. In contrast, drug discovery involves exploration of structure activity relationships (SAR) of hundreds or thousands of similar molecules in the same protein binding site. There is a richness and subtlety to a dense bioactivity landscape, it contains implicit redundancy over a large descriptor space and number of dimensions. For example, activity cliffs are small changes in ligand structure leading to unexpected outcomes in bioactivity and are relevant in drug discovery and can be traced to the 3D description . We hypothesize that it is necessary to learn from highly dense and redundant protein-ligand datasets to understand the rugged 3D landscape of bioactivity and identify the meaningful origins of activity versus memorizable patterns in the data. Accordingly, ML/DL affinity prediction models trained on more dense training data with more generalizable aims will need alternative and improved external testing, closer to real world drug discovery scenarios.