Comparisons of all the single models, ensembles, and knowledge distilled models on redocking and crossdocking are provided in Table and 2, respectively. We show the best performing single models, across both tasks, for the non-KD and KD trained models visualized as circles and triangles, respectively. Ensemble KD, shown by the arrows, reduces the computational overhead of the highest performing ensembles (denoted by the "X"s), while having better or comparable rescoring performance on both tasks than any non-KD single model. First, we compare the performance of the distilled Default2018 ensembles to the non-KD single models. Overall, the distilled Default2018 ensemble models do not have large differences from their non-KD trained counterparts. The distilled general default2018 ensemble shows slightly reduced performance on the redocking task, while the other Default2018 models show a slight increase in performance on the redocking task (Figure , S7a, and S8a).