Both compounds covalently bind to the mutated cysteine residue of this previously deemded "undruggable" target. The last years also witnessed various advances in the development of covalent ligands inhibiting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. During the last decades, advances in computing resources and methods have led to an increased utilization of computational techniques in medicinal chemistry. Understanding the interactions between ligands and proteins has become easier, faster and cheaper thanks to structure-based computer-aided drug design (SB-CADD) tools. These include molecular docking algorithms, used for predicting the binding mode of a ligand to its macromolecular target. The need for docking codes able to treat covalent inhibitors and to represent the formation of the covalent bond has been recognized. Therefore, popular codes such as AutoDock (AD), Glide (CovDock), FITTED, ICM-Pro and GOLD now include a covalent docking option. Besides, other programs have been specifically developed for covalent docking, such as CovalentDock (built on AutoDock), Cov DOX, Covalent CDOCKER, WIDOCK, and HCovDock. Efforts are undertaken to improve simplicity and accessibility of computational screening for covalent ligands, for example by creating freely available webservers, such as SCARdock. The majority of these algorithms include the bond between the ligand and the protein at the docking initialisation and consist solely in optimizing the ligand pose based on the non-bonded interactions with the pocket. Some have successfully been used to develop new hits by virtual screening, or to refine the interactions of a ligand with its target. However, in a benchmarking study it was found that re-docking success rates of covalent docking codes reached only 40-60%, which leaves room for improvement.