Overall, although the simplistic platform demonstrates a false positive detection rate (11.2%, Suppl. Figure ) comparative to the advanced approach (11.6%), it does so at the expense of missing real ligandable sites. Covalent ligandability assessment against a list of buried cysteine residues from the apo-covPDB dataset which are empirically confirmed to be ligandable was compared across two different methods. The first approach (A) entirely dependent on predictions made by an ML algorithm 32 was able to rank 28% of the buried cysteines as first or second most ligandable in the queried targets. The success rate of cysteine ligandability detection of the algorithm on the queried dataset is slightly higher when taking the probability score it assigns to each cysteine (by setting 3 bins for clarity: high for probability score 1.0-1.2, Medium for probability score of 0.8-1.0 and Low for less than 0.8). Details and exact score can be found in the SupplementaryFile1 (B) The second approach uses a combination of chemoproteomics-based (empirical) covalent sites and in-silico ligandability assessment techniques presented in this work. In the combined approach, the simplistic ligandability approach was able to capture 78% of the ligandable cysteines compared to the advanced method which correctly judged 92% of the empirical sites.