McNutt et al. observed that an ensemble of CNN models achieves the best binding pose ranking performance but requires longer running times compared to a single model, especially when used without a GPU (458 s and 72 s for the best ensemble and single model, respectively). When molecular docking is used for high-throughput screening, docking upwards of tens of millions of molecules, reducing computational cost is of foremost importance. Knowledge distillation (KD) was developed to condense the knowledge of a large neural network into another, smaller neural network for faster inference with similar model performance. The small model, the "student," is trained to match the representation of the large neural network, the "teacher," on the training dataset of the teacher. Ensemble KD transfers the knowledge learned by multiple teacher models to a single student model by minimizing the discrepancy between the average representation of the teachers and the student. Ensemble KD could therefore significantly reduce the computational overhead of workflows that use an ensemble of large models. We evaluate Ensemble KD to condense the pose ranking power of CNN model ensembles into a single model, thereby decreasing the molecular docking time. Our work shows ensemble KD produces more powerful single models than non-KD model training. We find the distilled models performance is correlated with pose score variation within the teacher ensemble.