To benchmark the performance of the ML-boosted approach on giga-scale chemical libraries, such libraries first need to be screened using standard brute-force docking to obtain the baseline data. Such docking data on the giga-scale has so far at least rarely been made publicly available: To date, the only example we are aware of are the Covid-19 screening results obtained with AutoDock-GPU on the Oak Ridge National Laboratory Summit computer. Herein, we used the fast Glide HTVS method in one of the largest conventional docking campaigns performed to date, to provide a brute-force docking baseline for analysis and comparison with our ML-boosted approach. We selected two distinct targets based on ongoing academic drug discovery projects: