Screening procedures for ranking and selection have received less attention than selection procedures, yet they serve as a cheap and powerful tool for decision making under uncertainty. Research on screening procedures has been less active in recent years, just as the advent of parallel computing has dramatically reshaped how selection procedures are designed and implemented. As a result, screening procedures used in modern practice continue to largely operate offline on fixed data. In this tutorial, we provide an overview of screening procedures with the goal of clarifying the current state of research and laying out opportunities for future development. We discuss several guarantees delivered by screening procedures and their role in different decision-making settings and investigate their impact on screening power and sampling efficiency in numerical experiments. We also study the implementation of screening procedures in parallel computing environments and how they can be combined with selection procedures.