Ensemble KD trains a student model on the representation of several teacher models, illustrated in Figure . GNINA provides four variants of CNN models for molecular docking; each variant is trained five times using different random seeds. These five models form an ensemble for each variant. Our ensemble KD utilizes the five teacher models to train a single student model with the same architecture as the teacher models. Additionally, we consider one more ensemble of the CNN models: "All Default2018 Ensemble", consisting of all scoring functions with the Default2018 architecture. During KD, the student model is trained on the same dataset as the teachers were trained on. For the All Default2018 Ensemble, we train the student on the CrossDock2020 v1.3 dataset since this is largely a superset of the other training datasets. Overall, 6 single models are distilled from 5 ensembles (Table ). The teacher models predict both a probability that the pose is high quality, ŷT pose , and an affinity, ŷT affinity , which are trained on the ground truth pose, y pose , and affinity, y affinity , using cross entropy (1) and hinged mean squared error (2), respectively.