G-Protein Coupled Receptors (GPCR) are involved in all the major signaling pathways. As a result, they often serve as potential target for therapeutic drugs. In this study we analyze publicly available assays involving different classes of GPCR to identify false positives. Using the latest developments in Machine Learning, we then build models that can predict such compounds with high confidence. Given the ubiquity of GPCR assays, we believe such models will be very helpful in flagging potential false positives for further testing.