Multi-Agent Pathfinding (MAPF) is the problem of finding paths for n agents in a graph such that each agent reaches its goal vertex and the agents do not collide with each other while moving along these paths. While different problem statements of MAPF exist, we are focused on MAPFR (Walker, Sturtevant, and Felner 2018), in which actions’ durations can be non-uniform, agents have geometric shapes, and time is continuous. Continuous-time conflict-based search (CCBS) (Andreychuk et al. 2019) is a recently proposed algorithm for finding optimal solutions to MAPFR problems. In this work, we propose several improvements to CCBS based on known improvements to the Conflict-based search (CBS) algorithm (Sharon et al. 2015) for classical MAPF, namely Disjoint Splitting (DS), Prioritizing Conflicts (PC), and high-level heuristics. We evaluate the impact of these improvements experimentally on both roadmaps and grids. Our results show that CCBS with these improvements is able to solve significantly more problems.