Although the protein data bank (PDB) provides one of the most detailed descriptors on protein structures, it is imperative to emphasize that such structures are only simplified snap-shot models of the target macromolecules. Regeneration of these structures into conformer archetypes that contain slight perturbations in residue side chain coordinates (using tools such as CONCOORD ) can reveal transitory structural patterns for opportunistic pocket detection while preserving macromolecule's unique architectural style. CONCOORD produces protein conformers around an experimental structure using geometric restrictions. Studies of Molecular Dynamics (MD) simulations indicate that collective degrees of freedom are crucial to protein conformational changes, which are often vital to protein function. These internal constraints and configurational barriers can be used be used to simulate conformers without the need for more CPU intensive MD simulations . Conformer generation using CONCOORD has been used in a similar fashion before however we report its first use in covalent site ligandability assessment. Molecular structure descriptors such as PDB files are not to be thought of as a set of fixed coordinates but rather a framework for generating hypotheses based on molecular patterns to be explored . The recent decade witnessed development of powerful generative AI models trained on omics data leading to algorithm that can recognize molecular features when faced with new data bearing similar characteristics. Our choice of DeepPocket as the pocketpredicting platform was inspired by a combination of its reliability on long-established methods, top-level performance and partitioned architecture which allowed us to further develop it towards detection of cryptic sites.