Chemokine receptors CCR3, CCR4, and CCR5 are G protein-coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration-approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R2 = 0.8), CCR4 (ROC, 0.98; R2 = 0.57), and CCR5 (ROC, 0.96; R2 = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration-approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC50, 0.7 μM) and CCR4 (IC50, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC50, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein-coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. SIGNIFICANCE STATEMENT: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein-coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.