We demonstrate the use of simulation systems for studying diagnostic problem solving. In particular, we present results from two empirical studies in which students diagnosed faults that occurred in a computer-based, dynamical simulation of an oil-fired marine power plant, called Turbinia. Our studies were shaped by a view of diagnosis as dual problem space search (DPSS), in which non-routine diagnosis was characterized as a process of generating hypotheses to explain the observed faults, and testing these hypotheses by conducting experiments. In the first study, we found that the less efficient students conducted significantly more experiments, indicating a strong bottom-up bias in their diagnostic strategy. In the second study, we examined the effects of imposing external resource bounds on students’ diagnostic strategies. Results indicated that constraints on diagnosis time led to a reduction in the number of actions performed and components viewed, without appearing to affect diagnostic performance. Constraints on the number of diagnostic tests reduced search in the experiment problem space, which appeared to negatively affect performance. Taken together, these results suggest that students’ diagnostic strategies were sensitive to constraints present in the software simulation system. As such, the results have important implications for the design of interactive learning environments for fostering strategies that are faithful to the activity demands of real-world situations.