Inhibition of streptococcal cysteine protease has recently emerged as quite a promising target to treat severe cases of Group A Streptococcus infections. For the identification of streptococcal cysteine protease inhibitors, structure-based virtual screening (SBVS) of ZINC Database was performed. The docking protocol was performed with the help of AutoDock Tools and AutoDock Vina software. Based on binding affinity and similarity of interactions with our target receptor streptococcal cysteine protease, 4 hit compounds were identified, which were further subjected to ADMET (Adsorption, Distribution, Metabolism, Excretion, Toxicity) and Drug-likeness to identify the best hit compound. The most potent compound showed binding of -7.7 KJ/mol with receptor streptococcal cysteine protease. It also showed 6 similar amino acid interactions with the receptor’s native ligand along with good ADME and Drug-likeness properties. Furthermore, the molecular dynamics simulation analysis revealed that the complex formed between the protein streptococcal cysteine protease and the hit compound ZINC000205429716 had good structural stability. The current study reveals the successful use of in silico SBVS methods for the identification of novel and possible streptococcal cysteine protease inhibitors, with compound ZINC000205429716 serving as a potential lead for the creation of Group A Streptococcus inhibitors.