Alzheimer's disease is a complex and recalcitrant condition that has largely evaded traditional molecular drug discovery approaches. Phenotypic drug discovery using high-content cellular models with unbiased small molecule screening is promising but faces obstacles from subtle signal, artifacts, and non-specific visual markers. We propose two deep learning-based methods to overcome these challenges in large-scale cellular screens. First, we develop deep neural networks to generate missing fluorescence channel images from an Alzheimer's disease high-content screen (HCS), enabling the identification and prospective validation of overlooked but active small molecules. This is a unique application of generative images in drug discovery. Second, we introduce a learned biological landscape using deep metric learning to organize drug-like molecules by live-cell HCS images. Metric learning outperforms conventional image scoring and reveals previously hidden molecules that push diseased cells toward a healthy state as effectively as positive control compounds. These results indicate that a wealth of actionable biological information lies untapped but readily available in HCS datasets.