Growing resistance of prevalent antitubercular (antiTB) agents in clinical isolates of Mycobacterium tuberculosis (MTB) provoked an urgent need to discover novel antiTB agents. Enoyl acyl carrier protein (ACP) reductase (InhA) from Mtb is a well known and thoroughly studied as antitubucular therapy target. Here we have reported the discovery of potent antiTB agents through ligand and structure based approaches using computational tools. Initially compounds with more than 0.500 Tanimoto similarity coefficient index using functional class fingerprints (FCFP_4) to the reference chemotype were mined from the chemdiv database. Further, the molecular docking was performed to select the compounds on the basis of their binding energies, binding modes, and tendencies to form reasonable interactions with InhA (PDB ID=2NSD) protein. Eighty compounds were evaluated for antitubercular activity against H37RV M. tuberculosis strain, out of which one compound showed MIC of 5.70 microM and another showed MIC of 13.85 microM. We believe that these two new scaffolds might be the good starting point from hit to lead optimization for new antitubercular agents.