Additionally, since the Dense architecture has a higher number of parameters than the De-fault2018 architecture, we distill the dense ensemble into a Default2018 architecture. We compare the dense ensemble to Default2018 architecture distillation to the non-KD trained crossdock default2018 single models and ensemble since it has the same architecture and training dataset. The distillation into the Default2018 architecture shows about the same redocking performance as crossdock default2018 single models and a higher crossdocking performance relative to both the crossdock default2018 single models and ensemble.