Pathogenic bacteria are evolving at a much faster rate and have the ability to acquire new antibacterial resistance patterns. The most common pathogenic bacteria are now becoming increasingly resistant to available antibiotics. The CDC has suggested to find alternative therapeutics to combat the growing antimicrobial resistance. Thanks to technological development in sequencing platforms and sophisticated bioinformatics pipelines, it now easier to analyze large-scale genomic data and propose alternative and novel treatment options. Subtractive genomics is one such approach that mines whole genomic DNA for identification of potential drug target(s). This strategy employs various computational filters using databases and online servers to screen and prioritize certain candidate proteins. Each filter analyzes the whole proteome of bacteria under study in a step-wise manner. Initially, strainspecific paralogous and host-specific homologous sequences are subtracted from the bacterial proteome to remove duplicates and prevent cytotoxicity and autoimmunity related challenges. The sorted proteome is further refined to identify essential genes involved in crucial metabolic pathways of the pathogen and thus can be used as targets for treatment interventions. Functional annotation is carried out to elucidate the involvement of these proteins in important cellular processes, metabolic pathway, and subcellular location analyses are carried out for finding the probable cellular location of the candidate proteins in the cell. Proteins with certain physicochemical properties like favorable molecular weight, hydrophobicity, and pI are rendered fine drug targets, thus filter. Importantly, the scrutinized proteins are screened against FDA approved DrugBank to identify their druggability potential. Finally, molecular docking analyses of the novel druggable targets with already present drugs are carried out. Only then, the prioritized candidate proteins can prove to be promising candidates for novel drug design and development.