The take-the-best heuristic (TTB) is an often-used decision strategy that relies on controlled and limited information search, taking only those pieces of information into account that are necessary to come up with a decision. This paradigm has proven to be an extremely valuable tool to investigate the dynamics of memory retrieval when decisions have to be based on stored information. In almost all implementations of TTB, its use is based on artificially constructed stimulus material that has to be learned from scratch. Here, we present a new variant of the TTB paradigm that is ecologically valid and easy to implement, without an extensive learning phase, because the cue knowledge is already known to the participants and just has to be reinstated in a short training phase. Participants were told that they won a flight in a lottery and had to do preference judgments between possible travel destinations based on attributes like their distance from home. In a series of experiments, we consistently replicated the typical finding when using TTB, i.e., a systematic decrease of the decision time the smaller the number of attributes that had to be compared. The paradigm was also capable of reproducing newer findings related to the use of TTB like a systematic RT increase when more attributes are associated to the decision options than actually required for TTB. All in all, our paradigm proved to be a useful and easy to apply experimental tool suited to capture the memory activation dynamics during heuristic decision making.