Enforcing terminology constraints is less straight-forward in neural machine translation (NMT) than statistical machine translation. Current methods, such as alignment-based insertion or the use of factors or special tokens, each have their strengths and drawbacks. We describe the current state of research on terminology enforcement in transformer-based NMT models, and present the results of our investigation into the performance of three different approaches. In addition to reference based quality metrics, we also evaluate the linguistic quality of the translations thus produced. Our results show that each approach is effective, though a negative impact on translation fluency remains evident.