Given the urgent need for new anti-staphylococcal agents due to bacteria's ability to develop resistance mechanisms and evade antibiotics, exploring the diversity and coverage in the chemical space of bioactive compounds is essential. Herein, we describe the chemical space of S. aureus FabI inhibitors that were the basis to generate new chemical structures through transformation rules based on highly active compounds, and the development of libraries focused on FabI. Focused libraries are compound collections designed using ligand-based insights, where undesirable features are removed, and several structural combinations are systematically explored . As described in this work, we constructed the library by ranking structure similarity and predicted activity using machine learning (ML) models to prioritize the assessment of new anti-staphylococcal agents. To the best of our knowledge, there are limited reports of ligand-based studies that employ similarity models and QSAR using ML in S. aureus.