During the past decade, the rapid advancement of high-throughput technologies has reshaped modern biomedical research by vastly extending the diversity, richness, and availability of data and methods across various domains. Currently, computational researchers are empowered with data, methods, and tools that allow for the possibility of making important contributions in biomedicine –– through primary analysis of pre-clinical and clinical datasets, the application and development of novel machine learning algorithms towards task automation and diagnostic or treatment predictions, and secondary analysis of existing public omics data. Here we discuss the challenges and pitfalls researchers from dry labs are facing and how they are gaining independence and leading high impact projects.