Multiple assessment directed novelty search (MADNS), introduced by the authors in [20], is an extension to the novelty search algorithm which exploits the observation that populations optimised for phenotypic novelty may contain solutions to multiple independent and conflicting objectives. It has been shown that, through the application of MADNS, an evolutionary trajectory may be simultaneously directed towards multiple conflicting objectives. Previous results from a series of simulated maze navigation experiments have shown that MADNS may significantly outperform novelty search in domains where the potential for phenotypic exploration is high [20]. In this paper we further explore the MADNS algorithm, assessing the effect upon the diversity and performance of the population as the phenotypic landscape increases. A series of experiments in domains with multiple conflicting objectives and expanding areas of irrelevant space show that the relative performance gain of MADNS increases alongside the potential for exploration. We conclude that, as the potential for exploration within a domain expands, the importance of directing novelty becomes ever more necessary.