In work by , the authors demonstrated that training a property predictor on the latent space and forcing the model to consider the quality of its predictions as part of its overall goal leads to an ordering of the latent space, which is desirable for generating candidates with particular features. Because latent spaces tend towards dimensionalities on the order of 30 or above, this orderedness has been visually presented through projection onto a two-dimensional space, often by using principal components analysis (PCA) to identify the most relevant variables. Since PCA is a linear projection, it remains unclear how well this visual orderedness is preserved in the high-dimensional space, and therefore we make a point to address the trustworthiness of this simple, rapid, and useful analytic technique.