Computer-aided drug design (CADD) techniques are often used in the early stages of drug discovery to identify lead molecules with high predicted binding affinity against a specific target. Virtual screening (VS) programs such as DOCK and Glide computationally dock and score libraries of compounds, producing ranked lists of molecules and their predicted protein-ligand structures. The top-scoring molecules are typically clustered by chemical structure and often filtered against known binders to discover novel chemotypes. Additional filtering steps based on interaction fingerprints or alternative scoring methods may be applied during this step as well. The remaining best-scoring cluster heads are often visually inspected ("hit-picked") by expert computational medicinal chemists to evaluate the quality of the generated poses before selecting final compounds for experimental testing (Fig. ). The current VS pipeline suffers from several bottlenecks where potential binders may be lost, leading to false negatives. The first is docking itself, wherein good candidate molecules score poorly by the running function or are not included in the screen. The latter problem is only growing more relevant as the size of available molecular libraries increases into the order of billions and beyond. Several ultra-large screens have evaluated hundreds of millions to billions of molecules, but docking the total available chemical space remains intractable. Efforts to accelerate docking programs for brute force screening rely on parallelizable operations performed with large supercomputing clusters and are inaccessible to the broader research community. One alternative approach to address this issue is active learning (AL). Active learning is a semi-supervised machine learning technique for training models with minimal labeled data, particularly when labels are expensive to obtain. In AL, the model is first trained on a random subset of the data. The model then predicts labels for and selects examples from the remaining unlabeled data for submission to the "oracle" to obtain true labels. These examples and labels are then added to the training set for the next iteration (Fig. ). Intuitively, this approach can identify the most informative examples at each iteration, constructing an accurate model from minimal data. The "oracle" is often a human expert, motivating the term human-in-the-loop training. In virtual screening, AL has been used to select regions of chemical space to dock from large libraries. The oracle is the docking program, and the model learns to select compounds to build and dock. Yang et al. demonstrated the success of this approach across multiple receptors both retrospectively and prospectively for UCSF DOCK. Graff et al. applied AL to screens from small libraries (Enamine 10K) to ultra-large libraries (>100M). They showed improved recovery of top hits over random search for Glide and DOCK scores. Thompson et al. explored various AL parameters, including acquisition function, number of molecules sampled at each iteration, ML architecture, and initial sampling strategy for predicting the output of free energy perturbation (FEP) calculations. Their best-performing models were trained on only 6% of the total dataset and successfully recovered 75% of the top 100 molecules, substantially decreasing the required number of calculations. These studies support AL as an efficient paradigm for learning complex scoring functions.