In this paper, I investigate the potential explanatory value of computer simulations, which are often said to be suitable tools for the prediction, emulation, or imitation of phenomena, but not for their explanation. Similarly, they are described as providing brute force (number-crunching) methods that are helpful to investigate the behaviour of physical systems but less so when it comes to understanding them. Be this as it may, wrongly or not simulations seem to be raising specific problems concerning their potential explanatory value. I try to systematically analyze these problems and for this purpose use existing theories of explanations as analytical guidelines. I first investigate how simulations fare concerning truth (section 2) and proceed by analyzing whether they satisfy the deductivity and nomicity requirements, which play a central role in Hempel’s model of explanation (section 3). I also study whether simulations are appropriate vehicles for relevant causal information which a good explanation is expected to provide (section 4). I then analyze how much the informational repleteness and the inferential or computational stodginess of computer simulations are problems for the development of explanatory knowledge and our understanding of phenomena (section 5). I finally analyze how much computer simulations can have a unificatory role as good explanations arguably do (section 6). Overall, I try to analyze why computer simulations still raise specific problems concerning explanations while seemingly being able to meet the criteria that explanations should fulfil. I suggest that some of these reasons are to be found in the epistemology of explanatory activity, in methodological expectations towards good explanations, and in the specific uses that are made of computer simulations to investigate complex systems—in addition to the fact that computer simulations are not human-sized activities and that, as such, they may fail to provide first-person understanding of phenomena.