Early in the drug discovery process, screening tests are used to rapidly pinpoint drug candidates that will have a potent effect on the target pathology. Recently, screening has started to be conducted within living cells with sophisticated automated microscopy systems (HCS: High-Content Screening). HCS is getting much traction in the R&D pipeline as many relevant targets can only be affected by drug candidates within cells. However, there are limitations to current HCS systems, notably for resolving and studying protein-protein interactions, a class of in-cell biochemical phenomena that are very relevant to anticancer drugs development. We have developed a way to identify drug candidates that interfere with proteinprotein interactions by using an automated microscope to take pictures of fluorescent proteins in cells. For our studies we combine fluorescence lifetime and spectral images at image resolution sufficient to see organelles in cells. Our method allows us to build protein-protein binding curves that can be used to measure the efficacy of drug candidates. Because of the number of candidates that normally need to be tested, such an approach must be very fast as well as accurate. Currently available systems cannot achieve this requirement. With current instruments, a secondary screen of 6 compounds took us more than a month just to acquire the data. Our project proposes to develop a new prototype Fluorescence Lifetime Imaging Microscope attachment that will allow us to generate binding curves in a matter of minutes and that can be added to either a research grade fluorescence microscope or to most commercial HCS systems. It will be used to study three classes of protein-protein interactions relevant to anticancer drug research.