Learning coordinated behaviour in decentralised multi-agent reinforcement learning with sparse rewards presents significant exploration challenges. Whilst novelty bonuses encourage exploration, we identify a critical failure mode they create in sequential coordination tasks: coordination de-synchronisation, where agents repeatedly traversing earlier coordination points gradually exhaust their intrinsic motivation to revisit these critical locations. We hypothesize that the effectiveness of exploration strategies depends on two key task factors: coordination complexity and geometric revisit pressure. Our preliminary experiments confirm this: lifelong novelty bonuses deteriorate with increasing task complexity, while augmenting with episodic bonuses substantially improves performance. These findings motivate further theoretical investigation into coordination-aware intrinsic motivation for decentralized agents.