DL models for binding affinity prediction were reported using different architectures and representations. In brief, several key approaches to featurization have been pursued. One approach uses protein-ligand interaction fingerprints that are defined using the atomic interactions or contacts between protein and ligand that fall within a specific radius of the ligand . An alternative method uses voxels that encode atomic properties at 3D grid-points in the proteinligand binding site and combine with a 3D convolutional neural network (3DCNN). Finally, graph descriptions of the protein-ligand complex combined with graph convolutional neural networks (3DGNN), have proved efficient in comparison to 3D convolutions due to their 3D invariant encoding. Nevertheless, the increasing complexity of description and model architectures has not led to a significant improvement in performance. The latest described 3DGNNs do not significantly outperform the first generation of CNNs and more importantly none of the models reported show general predictivity power for new targets.