Here, we present the reactive docking protocol, a predictive method for irreversible ligand binding events based on the analysis of a modified NAC, which combines descriptions of the ground state ligand and receptor with a bias toward the incipient bond formation. This relatively simple model is advantageous because it requires neither prior modification of the ligand structure (a non-trivial effort for large chemical collections) nor distinct parameters for different reaction classes while still describing the energetics of a key thermodynamic step in the covalent modification. Additionally, because of the large contribution of the ground state character to this model, the modified forcefield is still responsive to the structural features of the ligand-protein complex that dominates noncovalent interactions. The reactive parameters described here, covering the reactive potential and scalings on pseudo-1-3 and 1-4 interactions across the incipient bond, were calibrated on an inverse docking task with a diverse set of warheads (β-lactams, boronic acids, chloroacetamides, and acrylamides), and different reaction classes (β-lactam addition, borylation, nucleophilic substitution, and Michael addition) targeting serine and cysteine residues. These parameters were then tested against a more diverse set containing both withheld examples from the training warhead classes, as well as out-ofdomain reactions, differing in the warhead, mechanism, and labeled residue, performing excellently at predicting the labeled residue. This highlights how the relative simplicity of this model avoids overfitting and affords a generalizable forcefield that does not require extensive prior knowledge of either warhead or reaction mechanism. This method was additionally validated on a virtual screening task on reactive fragments against reactive cysteines in three separate proteins. Here we demonstrate that in a fully predictive setting (i.e.: neither the site nor the ligand are known in advance) the method can provide an enrichment of binders (particularly strong binders) comparable to focused conventional virtual screenings. While the use of a fragment library for assessing the VS performance is intrinsically more challenging compared to drug-like ligands , the overall results in simultaneous prediction of both residue and ligands suggest that this protocol is very suitable for prospective VS campaigns. This method and the calibration protocol also serve as a baseline for future development. Force field parameters described here can be easily modified, allowing interested researchers to optimize the parameters for an individual target or warhead class if sufficient data is available. Similarly, with the availability of more diverse and structurally rich experimental datasets, we anticipate the performance of the model on ligand ranking can be improved significantly. Collectively, the results presented here confirm and expand the successful applications previously reported, suggesting this method can be readily applied to a diverse range of docking tasks and warheads, requiring only the reassignment of atom types and inclusion of the modified forcefield parameters. To the best of our knowledge, there is no other computational method that is capable of addressing both predictive aspects of proteomics experiments, that is predicting both residue to be modified and ligands capable of doing so. The very limited computational overhead and the predictive power make this method ideally suited for devising large virtual screenings campaigns to screen the growing commercially available chemical collections of reactive molecules. Moreover, by leveraging the structural data generated by AlphaFold , it makes possible proteome-wide labeling predictions, providing in silico support for large chemical proteomics applications.