The TEA domain (TEAD) transcription factors are important parts of the Hippo signaling cascade and are important therapeutic targets in cancer research because they help control cell growth, avoid apoptosis, and cause tumors to form. In this study, a structure-based virtual screening method was used to find new TEAD antagonists in the ChemDiv natural product database. Using the AutoDock platform for molecular docking, we ranked eight candidate ligands—16956, 726, 5271, 11768, 12384, 15598, 15641, and 3622—based on strong binding affinities, as shown by docking energies that ranged from –8.02 to –8.49 kcal/mol. Swiss ADME's full in silico ADMET profile showed that all of the selected compounds had good pharmacokinetic properties and did not break Lipinski's rule of five, which means they would be quite bioavailable when taken by mouth. Two lead candidates, 11768 and 15598, did not pass across the blood-brain barrier (BBB) and were not substrates for P-glycoprotein. This means that they had less exposure to the central nervous system and a lower chance of developing multidrug resistance. Later molecular dynamics (MD) simulations verified that the ligand–TEAD complexes were stable in their shapes, and MMGBSA (Molecular Mechanics/Generalized Born Surface Area) free energy calculations indicated that they had high-affinity binding. Principal component analysis and free energy landscape tests helped to explain even more the dynamic behavior and thermodynamic landscapes of the complexes. This integrated computational technique helped us find strong, drug-like TEAD inhibitors in a logical way. It also gave us a solid base for further preclinical testing and structural optimization in the creation of targeted anticancer drugs.