Due to the speed, efficiency, relative risk, and lower costs compared to traditional drug discovery, the prioritization of candidate drugs for repurposing against cancers of interest has attracted the attention of experts in recent years. Herein, we present a powerful computational approach, termed Prioritization of Candidate Drugs (PriorCD), for the prioritization of candidate cancer drugs based on a global network propagation algorithm and a drug-drug functional similarity network constructed by integrating pathway activity profiles and drug activity profiles. This provides a new approach to drug repurposing by first considering the drug functional similarities at the pathway level. The performance of PriorCD in drug repurposing was evaluated by using drug datasets of breast cancer and ovarian cancer. Cross validation tests on the drugs approved for the treatment of these cancers indicated that our approach can achieve area under receiver-operating characteristic curve (AUROC) values greater than 0.82. Furthermore, literature searches validated our results, and comparison with other classical gene-based repurposing methods indicated that our pathway-level PriorCD is comparatively more effective at prioritizing candidate drugs with similar therapeutic effects. We hope that our study will be of benefit to the field of drug discovery. In order to expand the usage of PriorCD, a freely available R-based package, PriorCD, has been developed to prioritize candidate anticancer drugs for drug repurposing.