Despite continuous pharmacologic advances, the treatment of schizophrenia remains challenging and suboptimal outcomes are still too frequent. There are currently limited new approved drugs without resistance. For this reason, drug repurposing presents a promising solution for identifying new therapeutic uses for schizophrenia. In this study, we provide a workflow of computational signature-based drug repurposing methodology.  We initially utilized a dataset from GEO (Gene Expression Omnibus), which consists of RNA sequence data from blood-derived leukocyte samples from individuals with schizophrenia and control subjects and conducted an analysis. The outcome of this was the identification of 1205 statistically significant DEGs, of which only 150 upregulated and downregulated genes were used in both tools CMap and L1000CDS².  Each of these databases produced as an output, a catalog of potential compounds that can counter disease’s signature and therefore have therapeutic effects for schizophrenia. Subsequently, only the compounds, associated with the disease according to the research, were chemical clustered, and then their mode of action were predicted.  In the last stage, we conducted a literature review to evaluate their relationship of these MOAs with the disease. This systematic analysis provides a list of potential drugs for schizophrenia treatment that their efficacy can be evaluated in the wet-lab experiments, the next stage of drug repurposing.