Neural Machine Translation (NMT) models tend to achieve best performance when larger sets of parallel sentences are provided for trai- ning. For this reason, augmenting the training set with artificially-generated sentence pairs can boost performance. Nonetheless, the performance can also be im- proved with a small number of sentences if they are in the same domain as the test set. Accordingly, we want to explore the use of artificially-generated sentences along with data-selection algorithms to improve German- to-English NMT models trained solely with authentic data. In this work, we show how artificially- generated sentences can be more beneficial than authentic pairs, and demonstrate their ad- vantages when used in combination with data- selection algorithms.