Direct Multisearch (DMS) is a well-known multiobjective derivative-free optimization class of methods, with competitive computational implementations that are often successfully used for benchmark of new algorithms and in practical applications. As a directional direct search method, its structure is organized in a search step and a poll step, being the latter responsible for its convergence. A first implementation of DMS was released in 2010. Since then, the algorithmic class has continued to be analyzed from the theoretical point of view and new improvements have been proposed for the numerical implementation. Worst-case-complexity bounds have been derived, a search step based on polynomial models has been defined, and parallelization strategies have successfully improved the numerical performance of the code, which has also shown to be competitive for multiobjective derivative-based problems. In this talk we will survey the algorithmic structure of this class of optimization methods, the main theoretical properties associated to it and report numerical experiments that validate its numerical competitiveness.