Despite the approval of two first-in-class anti-tuberculars over the past two decades, the global burden of tuberculosis (TB) remains unacceptably high, in part due to the emergence and spread of drug-resistant strains of Mycobacterium tuberculosis (Mtb). This review summarizes advances and ongoing challenges in anti-TB drug discovery, focusing on identifying and validating novel targets. Highlighted is a framework developed by the TB Drug Accelerator (TBDA) consortium for target validation in Mtb. Two computational platforms, DAIKON and PARSNIP, allow the systematic evaluation of targets across multiple dimensions, including chemical validation, genetic essentiality, vulnerability, and the feasibility to identify drug-like molecules for a target of interest. Case studies of Pks13 and NadE illustrate how these parameters guide target prioritization and risk assessment. By integrating these metrics, the framework enables dynamic, transparent target ranking, supporting development of both pan-TB and treatment-shortening regimens. This paradigm is adaptable to other bacterial pathogens and is designed to improve evidence-based decision-making in antibacterial drug discovery.