Multiobjective optimization problems are characterized by having multiple conflicting objective functions that cannot be all optimized simultaneously. Instead of a single optimal solution, these problems have multiple, so-called Pareto optimal solutions that represent different trade-offs. They cannot be ordered from best to worst and it is the task of a domain expert, a decision maker, to select the most preferred Pareto optimal solution. Many multiobjective optimization methods exist to support a decision maker in finding their most preferred solution. These methods utilize preference information expressed by a decision maker, e.g., desirable objective function values. Interactive multiobjective optimization methods are of particular interest because they put the decision maker at the center of the solution process, allowing them to express preferences and study corresponding solutions iteratively. This enables the decision maker to learn about the problem being solved and the feasibility of their preferences. However, interactive methods come with many challenges, most of which are related to the need of further support for a decision maker in directing the solution process with their preferences. In this chapter, we survey and categorize the ways explainability has been leveraged in interactive methods and discuss its potential to offer better support to a decision maker in tackling multiobjective optimization problems.