Drugs play a crucial role in treating numerous diseases. However, not all compounds have the properties required to function effectively within biological systems. Lipinski’s rule of five, a widely accepted guideline, helps assess drug-likeness by screening for molecular properties associated with bioavailability. To enhance the efficiency of computational drug discovery, especially for docking studies with viral targets, filters are applied to the vast array of compounds in public databases like PubChem. By excluding compounds unlikely to be effective as therapeutic agents, researchers can focus on those with higher potential, streamlining subsequent, computationally intensive analyses. This chapter introduces a python-based workflow for filtering PubChem Compounds, prioritizing those that meet Lipinski’s criteria and are more likely to demonstrate biological efficacy. This approach can be used as an example for targeted and efficient drug development efforts.