Overall, covalent site ligandability assessment using a simple distance (10Å) threshold approach on rigid protein structural models demonstrated a competent detection performance of 85% when tested on holo site and satisfactorily (72%) when tested on apo site data (Figure ). These scores correlate with formerly reported F-pocket and DeepPocket binding site predictions on holo and apo protein structures, respectively . Generally, detection performance of many algorithms including F-pocket, Kalasanty, DeeplyTough, DeepSite, and DeepCoSI albeit considerate of signature structural features representing dynamic protein regions, exhibit suboptimal detection performance on holo sites as demonstrated in this study. The inability of the simplistic approach to capture ligandability of apo sites could associated with multiple factors including mitigation of high false positive rates and inability to recognize unstructured features used in training the pocket detection algorithms. Apo sites are expected to possess lower solvent accessible surface area (SASA), unstructured architecture of the ligand cavity and imprecise positions of pocket-forming atoms from the ideal expected coordinates of the ligandable site. Despite their consideration of protein dynamics, these distinct features could pose challenges for algorithms trained on holo pockets, especially when identifying valid sites within apo structures.