In this work, we established the effectiveness of KD in reducing the cost of CNN model ensembles used by GNINA for scoring protein-ligand binding conformations while maintaining their pose ranking superiority over non-KD-trained single models. Upon applying ensemble KD, the Top1 performance for the redock default2018, All Default2018, and dense distilled models in redocking and crossdocking tasks landed between the best-performing single models and their corresponding ensembles (Figures S7, S9, and S10). The distilled general default2018 model did not outperform the best single model for redocking; however, the distilled models show better performance than the single models for crossdocking (Figure ). The crossdock default2018 distilled models showed the opposite trend, with increased performance on redocking tasks and decreased performance on crossdocking tasks over the single models (Figure ). We hypothesize the failing of these distillations is due to a lack of diversity among the single model pose outputs in these ensembles (Fig-