Since dye-sensitized solar cells (DSSCs) appeared as a promising inexpensive alternative to the traditional silicon-based solar cells, DSSCs have attracted a considerable amount of experimental and theoretical interest. In contrast with silicon-based solar cells, DSSCs use different components for the light-harvesting and transport functions, which allow researchers to fine-tune each material and, under ideal conditions, to optimize their overall performance in assembled devices. Because of the variety of elementary components present in these cells and their multiple possible combinations, this task presents experimental challenges. The photoconversion efficiencies obtained up to this point are still low, despite the significant experimental efforts spent in their optimization. The development of a low-cost and efficient computational protocol that could qualitatively (or even quantitatively) identify the promising semiconductors, dyes, and electrolytes, as well as their assembly, could save substantial experimental time and resources. In this Account, we describe our computational approach that allows us to understand and predict the different elementary mechanisms involved in DSSC working principles. We use this computational framework to propose an in silico route for the ab initio design of these materials. Our approach relies on a unique density functional theory (DFT) based model, which allows for an accurate and balanced treatment of electronic and spectroscopic properties in different phases (such as gas, solution, or interfaces) and avoids or minimizes spurious computational effects. Using this tool, we reproduced and predicted the properties of the isolated components of the DSSC assemblies. We accessed the microscopic measurable characteristics of the cells such as the short circuit current (J(sc)) or the open circuit voltage (V(oc)), which define the overall photoconversion efficiency of the cell. The absence of empirical or material-related parameters in our approach should allow for its wide application to the optimization of existing devices or the design of new ones.