Virtual screening can identify novel chemical starting points, but the workflow connecting docking, diverse filtering, and expert hit selection is largely manual. This fragmentation becomes especially limiting when screening campaigns expand from focused libraries to ultra-large chemical spaces. Current pipelines often require separate tools, manual file transfers, and subjective expert triage. These discontinuities can slow turnaround and make decision-making inconsistent from one project to another. This article proposes an agentic AI workflow that could autonomously manage the virtual screening cascade. The system docks a compound library, re-scores poses with learned models, filters candidates using multi-parameter ADMET profiles, and presents a prioritized, explainable hit list to a human expert for final triage. The proposed framework includes a docking job manager, a machine-learning re-scoring model, an ADMET prediction suite, a compound selection engine, a human-review dashboard, and an orchestrator agent. These modules would coordinate through standardized interfaces while preserving audit trails for each decision. Such a system would be expected to reduce repetitive data processing and improve consistency in screening documentation. It would allow medicinal chemists to focus attention on high-value judgment calls rather than routine docking, filtering, and ranking operations. An agentic virtual screening workflow could broaden access to advanced computational screening. By combining automation with expert oversight, it could support continuous and adaptive screening across multiple targets.