Controlling units in real-time strategy (RTS) games is a challenging problem in Artificial Intelligence (AI) as these games are fast-paced with simultaneous moves and massive branching factors. This paper presents two extensions to the algorithm UCT Considering Durations (UCTCD) for finding optimal sequences of actions to units engaged in combat using the RTS game StarCraft as a test bed. The first extension uses a script-based approach inspired by Portfolio Greedy Search and searches for sequences of scripts instead of actions. The second extension uses a cluster-based approach as it assigns scripts to clusters of units based on their type and position. Our results show that both our script-based and cluster-based UCTCD extensions outperform the original UCTCD with a winning percentage of 100% with 32 units or more. Additionally, our results show that unit clustering gives some improvement in large scenarios while it is less effective in small combats. We suggest further research of the behavior and possible variants of the cluster-based approach which can be applied to many other algorithms similarly to UCT. The algorithms were tested in our StarCraft combat simulator called JarCraft, a complete Java translation of the original C++ package SparCraft, made in hopes of making this research area more accessible.