We also noted that GPUs were more sought after in our computing environment and wait times in the queue were consequently often longer. Idle wait times between iterations can be another argument to opt for a larger docking fraction and fewer iterations. On the other hand, it is also possible to run the prediction step on CPU. For instance, a prediction run utilizing 1280 CPUs in our environment took 29.7 hours. Thus, predictions on GPUs were around 2-6 times faster in our hands (depending on parallelization and number of utilized GPUs/CPUs). However, if CPUs are more easily accessible to a user in great bulk or utilizing CPUs cuts down idle wait times, prediction on CPUs is a suitable alternative. Taken together, depending on resource availability, HASTEN offers the user options to balance the computational load between CPUs (typically used for docking, also predictions at reduced speed) and GPUs (training and faster prediction) and we, herein, presented some examples of how to adapt the pipeline for a specific system and configuration.