Traditional approaches to drug discovery are generally regarded as protracted and costly. The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development. Gene expression microarrays are regularly and broadly applied in clinical studies of human diseases, providing genome-wide characterization of a disease state. Microarrays are also widely used to discover gene expression patterns that signify pharmacologic perturbation, allowing for the development of high-quality signatures of drug effect. A pair of papers from one group recently published in Science Translational Medicine provided a concrete example of how to using bioinformatics approach to reinterpret and compare genome-wide gene expression data, that allows us to effectively hypothesize which drugs from one disease-indication can be repurposed for another disease. They examined publicly available gene expression data and determined the genes affected in 100 diseases and 164 drugs. By pairing drugs that correct abnormal gene expression in diseases, they confirmed known effective drug-disease pairs and predicted new indications for already approved agents. Experimental validation that an antiulcer drug and an antiepileptic can be reused for lung cancer and inflammatory bowel disease reinforced the promise of this approach. These two drugs are therefore good candidates for repositioning to treat lung cancer and inflammatory bowel disease that in need of better therapies, and we now have a way to mine available data for fast routes to new disease therapies.