Testing in virtual road environments is a widespread approach to validate advanced driver assistance systems (ADAS). A number of automated strategies have been proposed to explore dangerous scenarios, like search-based strategies guided by fitness functions. However, such strategies are likely to produce many uninteresting scenarios, representing so extreme driving situations that fatal accidents are unavoidable irrespective of the action of the ADAS. We propose leveraging datasets from real drives to better align the virtual scenarios to reasonable ones. The alignment is based on a simple distance metric that relates the virtual scenario parameters to the real data. We demonstrate the use of this metric for testing an autonomous emergency braking (AEB) system, taking the highD dataset as a reference for normal situations. We show how search-based testing quickly converges toward very distant scenarios that do not bring much insight into the AEB performance. We then provide an example of a distance-aware strategy that searches for less extreme scenarios that the AEB cannot overcome.