AI is a transformative force in drug discovery. It now faces a "trial by fire"as it confronts the complexities of real-world medical challenges. Only rigorous experimental validation will bridge the gap between AI's predictions and its real-world applications. An active feedback loop between silico and experimental data is crucial to ensure we verify the integrity of AI findings, train algorithms, and adapt to and optimize for safe and effective clinical outcomes. Attendees will walk away from this session with the complexities that must be considered when working with AI and its breakthrough innovations.