Chemogenomics knowledge-based drug discovery approaches aim to extract the knowledge gained from one target and to apply it for the discovery of ligands and hopefully drugs of a new target which is related to the parent target by homology or conserved molecular recognition. Herein, we demonstrate the potential of knowledge-based virtual screening by applying it to the MDM4-p53 protein-protein interaction where the MDM2-p53 protein-protein interaction constitutes the parent reference system; both systems are potentially relevant to cancer therapy. We show that a combination of virtual screening methods, including homology based similarity searching, QSAR (Quantitative Structure-Activity Relationship) methods, HTD (High Throughput Docking), and UNITY pharmacophore searching provide a successful approach to the discovery of inhibitors. The virtual screening hit list is of the magnitude of 50,000 compounds picked from the corporate compound library of approximately 1.2 million compounds. Emphasis is placed on the facts that such campaigns are only feasible because of the now existing HTCP (High throughput Cherry-Picking) automation systems in combination with robust MTS (Medium Throughput Screening) fluorescence-based assays. Given that the MDM2-p53 system constitutes the reference system, it is not surprising that significantly more and stronger hits are found for this interaction compared to the MDM4-p53 system. Novel, selective and dual hits are discovered for both systems. A hit rate analysis will be provided compared to the full HTS (High-throughput Screening).