Making the transition towards higher education can be a daunting task for any (future) student. Especially in education set ups with low admission fees and open access, the possibilities towards higher education are almost limitless. As such, assisting students in this choice by presenting them with a manageable list of study programs (advice set) that truly matches their interests can provide a big bonus. But which criterion should be used when presenting advice sets of study options in order to orient students towards study programs that match their vocational interests? And how long should such a list of possible study options be? In an attempt to answer these questions, the current research introduces an Empirical Advice Set Engine (EASE) to develop a new methodology, optimizing the process of matching prospective students to study environments while balancing the length of the presented list of study options to the quality of person-environment fit of these study options for each student. The features of the engine are explored using two large student datasets (N1 = 4,892 and N2 = 7,063). Analyses indicate that the present study introduces a very precise EASE, resulting in accurate threshold estimates to enable advice set creation. The balance between size and fit quality of the resulting advice sets of matching study programs for each student are compared to advice sets generated through more classic congruence index approaches. The newly presented method (vs. the classic methods) shows an increase in fit quality of about 6% in the presented advice set of study programs to future students, while keeping an equally long list. Also, applying EASE to successful and persistent students would orient about 81% of them towards the study program that they indeed chose years ago, without inflating the number of alternative choices in the generated advice set.