One objective in selecting targets for our case study was to ensure their distinct binding pocket properties. Since docking and, in particular, scoring success is target-dependent, any ML approach trained on docking scores will inherently reflect the same target dependence. In line with that, previous work with HASTEN, utilizing 12 literature targets, has already revealed different performances of the same protocol against different targets. However, having two targets with distinct properties assessed on the giga-scale would, for the first time, indicate whether the ML-boosted HASTEN performs equally well for both targets when benchmarked against the brute-force docking backdrop on the giga-scale and thus highlight whether potential additional dependencies arise from the choice of the ML method when employed on HASTEN's intended use-scale. To assess the pocket properties, we first computed pocket descriptors with Schrödinger SiteMap, as summarized in Table .