<p>In this paper, we provide an overview of exploration algorithms, a novel class of evolutionary computation approaches encompassing novelty search and quality-diversity, as they may be applied to portfolio optimization and related problems in quantitative finance. These algorithms rely on the idea of divergent search, prioritizing the discovery of novel, unexplored solutions over maximizing a classical objective (fitness) function. Their favorable exploration properties make them particularly valuable in evolutionary reinforcement learning, where they have been successfully applied to diverse practical domains such as procedural generation of video game content and robotics. However, their application in portfolio optimization (PO) and quantitative finance in general, remains largely unexplored. We posit that this gap presents significant untapped potential, given the natural connections between divergent search principles and key concepts in PO, including diversification, inter-individual variety of risk preferences, and sensitivity to market regimes. Following an introduction to exploration algorithms, we delve into existing research on their applications in PO, outlining their advantages and shortcomings. Moreover, we propose several research directions that are expected to pave the way for more substantial leverage of such algorithms in decision-making processes in PO and beyond, while emphasizing challenges that stand in the way of their realization.</p>