Chemical proteomics-based drug discovery research typically suggests a large number (1000s) of targets of which 1 to 5 potential targets are selected based on target quality for tractability analysis using wet-lab biochemical and biophysical validation assays. Target selection and prioritization is typically done using computational biology approaches that qualify targets using curated omics data available to us. Target prioritization is a long-standing issue in drug discovery research especially due to lack of techniques that assess binding site quality and downstream functional impact of targeting each site . Target validation is highly time consuming (est. >1year/target) and is a monetary sink (est. >$1M/target validation) in the covalent drug discovery pipelines. Quite often, such tests eventually indicate that one or none of the elected targets are true therapeutic targets and are indeed identified as off-targets and/or false positives. Aside from being highly time and resource wasteful, this traditional approach of target validation misses on a vast opportunity that the remaining unvalidated targets pose. Ligandability evaluation techniques showcased in this work provide an opportunity to explore more than 75% of known proteome (767,580 protein structures) using chemoproteomics-enabled covalent drug discovery experiments on a time and financial scale that is at least 100-fold less than the traditional approaches (Figure ). To ensure the platform's efficacy in eliminating false-positive covalent sites, computational tools were employed to identify and disqualify superficial matching-negative residues in apo-covPDB structures. The comprehensive assessment revealed the platform's robustness in minimizing false-positive rates, crucial for ensuring the accuracy of ligandability predictions.