With the improvement of people's living standards, obesity has increasingly become a major health problem for adults and children. Drugs are still the main treatment for obesity, but the number of existing drugs is limited and the effect can hardly meet the needs of all patients. While new anti-obesity drugs are in a difficult development situation, the use of drug repositioning (DR) strategy to explore new indications of existing drugs can provide ideas and directions for the discovery of new anti-obesity drugs. In recent years, the steady increase of biomedical data and the continuous development of high-throughput screening technology have shown great potential anti-obesity DR, which is achieved in a more systematic and rational way by computer-based computational approaches and experimental approaches based on high-throughput screening. The computational approaches are driven by data, combining the application of databases, network pharmacology and artificial intelligence. The experimental approaches have transited from low-throughput animal model-based techniques to high-throughput screening, providing opportunities to discover potential therapeutic drugs for obesity. Here, we review the research methods of DR and its application in the discovery of potential anti-obesity drugs.