Deep learning can automate the analysis of the hundreds of thousands of images produced by automated microscopy systems each day. High-content screening (HCS) systems that combine high-throughput biotechnology with automated microscopy are revolutionizing drug development and cell biology research. The images produced by these systems provide valuable insight into how cells respond to many chemical or genetic perturbations. Existing image analysis pipelines rely on hand-tuning the segmentation, feature extraction, and machine learning steps for each screen. For many research groups, tuning these pipelines remains a bottleneck in implementing HCS. We'll demonstrate how deep learning-based pipelines overcome this bottleneck and outperform existing methods. We'll show improved results on classifying sub-cellular protein localization in genome-wide screens of the GFP-tagged yeast collection.