In many domains, different actions have different costs. In this paper, we show that various kinds of best-first search algorithms are sensitive to the ratio between the lowest and highest operator costs. First, we take common benchmark domains and show that when we increase the ratio of operator costs, the number of node expansions required to find a solution increases. Second, we provide a theoretical analysis showing one reason this phenomenon occurs. We also discuss additional domain features that can cause this increased difficulty. Third, we show that searching using distance-to-go estimates can significantly ameliorate this problem. Our analysis takes an important step toward understanding algorithm performance in the presence of differing costs. This research direction will likely only grow in importance as heuristic search is deployed to solve real-world problems.