The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (<i>n</i> = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with <i>KRAS</i> wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in <i>KRAS</i> In addition, we confirmed the transforming potential of two novel fusions, <i>RRBP1</i>-<i>RAF1</i> and <i>RASGRP1</i>-<i>ATP1A1</i>, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.