Clinical reasoning is a central skill in diagnosing cases. However, diagnosing a clinical case poses several challenges that are inherent to solving multifaceted ill-structured problems. In particular, when solving such problems, the complexity stems from the existence of multiple paths to arriving at the correct solution (Lajoie, 2003). Moreover, the approach one employs in diagnosing a clinical case is in some measure dependent upon the complexity of the case. This leads us to the question: Are there differences in the manner in which novices solve cases with varying levels of complexity in a computer-based learning environment? More specifically, we are interested in understanding and elucidating if there are clinical reasoning differences in regards to accuracy, efficiency, and process across three virtual patient cases of varying difficulty levels. Examining such differences may have implications from both a learner modelling and system enhancement perspective. We close by discussing the implications for practice, limitations of the study and future research directions.