Recently, many researchers have focused on using machine and active learning (ML/AL) methods to facilitate ultra-large-scale screening. In many recent workflows, they dock a small subset of the chemical library to a target of interest and train an ML model to predict the docking score. They use this ML model to predict scores for the entire library and use a selection criterion to choose additional molecules to dock and retrain the ML model. This iterative process, encompassing successive cycles of docking, model training, and prediction, aims to enhance the ability to identify high-scoring molecules from the virtual library. Due to the ability of ML models to make rapid but accurate predictions of docking scores, this workflow can reduce the computation time required to screen ultra-large libraries. However, this approach has its limitations: