Previous work has found that ensembles of CNN models perform better at ranking binding poses, but require longer running times. We applied ensemble KD to condense the knowledge of these ensembles into a single CNN, resulting in shorter running times with higher pose ranking performance than any non-KD trained single model. This efficiency gain is more apparent when the teacher model has a large number of parameters or the ensemble is composed of a diverse set of models. We were able to distill an ensemble of 15 models into a single model, gaining about a 5x docking runtime speedup, with pose ranking performance higher than any other single CNN model with comparable computational cost on both redocking and crossdocking tasks.