This chapter aims to provide the reader with an overview of how to leverage computational algorithms toward improving the affinity of an antibody for a particular target. In most cases, structural information is required; however, the prerequisite information is not always available. Here, we take a bottom-up approach that will guide readers through scenarios where data such as antibody apo or complex structures are unknown. The focus of the chapter is on how computational tools and workflows can be used to address these limitations when attempting to identify an affinity-matured antibody variant with desirable properties. We begin with an overview of antibody structure prediction, followed by predictions of antibody-antigen complexes, antibody affinity, stability, and developability. Finally, we conclude this chapter with guidance notes and best practices for antibody computational design.