Microscopy is the oldest and most versatile technique for the study of cellular phenotype, but it has been underutilized for modern functional genomics and therapeutic discovery efforts due to traditional limitations on throughput. Recent advances have overcome these historical bottlenecks, however, and we have entered a new renaissance in microscopy, where innovations in imaging and machine learning have enabled the high-dimensional single-cell profiling of cellular images with unprecedented resolution and scale. Recently, we and others have developed combined in situ sequencing/multicolor labeling protocols that will enable the creation of image-based phenotyping pipelines of extraordinarily high throughput, at drastically reduced cost compared to existing single-cell profiling methods. By combining these pipelines with new machine learning approaches, we hope to unlock new insights about cancer vulnerabilities, enable the discovery of new therapeutics, and elucidate the role of human genetic variation in health and disease.