Once the geometry optimisation is completed, the remaining (low energy) conformers are scored to predict their binding affinity. There are many choices available for scoring binding poses and their corresponding binding affinities, and these are usually classified as either force-field, empirical, or knowledge based. In the latter case, input features (such as atomatom pairwise contacts) are used to train models to reproduce data for known proteinligand complexes. Recently, machine learning models have emerged, in which an arbitrary, nonlinear relationship between input and target prediction is learned. One such approach is the gnina convolutional neural network (CNN) model, which takes as its input features a 3D grid-based representation of the protein-ligand complex and the atom types. The model has been jointly trained for binding pose and affinity prediction on a cross-docked set containing examples of ligand poses generated by docking into multiple receptors. The resulting models are competitive with other grid-based CNN models, and outperform the traditional empirical Vina scoring function. They are available as part of the gnina docking software package, which is a fork of Smina 47 and AutoDock Vina. Here, we use gnina only for re-scoring the output ligand 3D structures, using the 'score only' flag and the default ensemble of CNN scoring models. Gnina CNNaffinity scores (predicted pK) are output, and compared with experimental binding affinity (where available).