In conclusion, HASTEN represents a robust approach to identify the bulk of the top-scoring virtual hits of a brute-force giga-scale docking campaign in a reduced time frame by reducing the required docking calculations by 99% (or more). In this work, HASTEN has been benchmarked on the giga-scale and shown to represent a viable strategy for ML-boosted docking to join a growing arsenal of methods designed to tackle the challenges associated with screening giga-scale libraries in everyday drug discovery.