A major goal in biotechnology is to be able to design/generate proteins while optimizing specific properties. Previous work has used probabilistic models of natural sequences sometimes together with labeled data to generate novel functional examples. These methods typically depend on identifying and aligning a set of proteins believed to have a similar function but the challenge is to know how narrow or broad to make the alignment. Furthermore, it is necessary to quantify how evolutionary information alone predicts and/or the number and types of labels are needed for designing functional and diverse sequences. We explore different model architectures using evolutionary sequences and sets of experimental labels to assess where labels are the most powerful; results are validated on existing published experimental data.