The main goal of this study was to construct antiviral libraries focused on molecular targets related to respiratory diseases. To achieve this, we trained, retrained and validated ML classification models using bioactivity data from ChEMBL 33. The predictive models were used to filter compound libraries from diverse sources. As part of the data preparation to train the ML models, the chemical data sets were analyzed and characterized in terms of chemical diversity and coverage in chemical space using chemoinformatics methods. The resulting antiviral-focused chemical libraries, which are freely available in the public domain, offer valuable starting points for further computational and/or experimental screening.