Ensemble KD proved markedly effective at reducing the cost of large ensembles while still maintaining much of the performance. Distilling the All Default2018 Ensemble, composed of 15 different models, into a single model proved effective for both redocking and crossdocking (Figure ). This distillation reduces the computational cost of docking from about 123 seconds to about 25 seconds (CPU-only docking), a 5x speedup, with only a small decrease in Top1 percentage on both tasks (Tables and). This distillation performance is notably While ensemble KD exhibits considerable potential for enhancing the efficiency of existing molecular docking CNNs, there is much room for further exploration. One of the key challenges is the current lack of understanding regarding which protein and molecule features contribute most significantly to the distillation process. Additionally, our work focused only on distilling the pose scoring of the CNN models as the KD field largely focuses on the distillation of classification tasks. Future work can explore simultaneously distilling the affinity prediction regression value using newly proposed regression distillation methods. Additionally, KD could be used for the distillation of increasingly large state-of-the-art molecular docking graph neural networks (GNNs) to reduce model computation with minimal performance impact.