Although an old idea , active learning (AL) methods have recently gained increasing attention for accelerating VS , either to enable screening very large libraries with docking , or screening smaller libraries with binding energy prediction or quantum chemical methods . VS-AL methods generically sample a small subset of compounds to evaluate with a desired scoring function, or oracle, and construct a surrogate model to predict the oracle score of as-yet unevaluated candidates. This model is used to select new candidates to screen, based on an acquisition function which might depend on the surrogate predictions, associated uncertainties and other factors. Evaluated molecules are used to retrain the surrogate model and the approach is iteratively repeated. Such approaches regularly claim hundred-fold or more accelerations over brute-force VS in terms of oracle calls needed to retrieve top-scoring hits.