In order to retrieve analogous designs for design-by-analogy, computational systems require the calculation of similarity between the target design and a repository of source designs. Representing designs as functional abstractions can support designers in practicing design-by-analogy by minimizing fixation on surface-level similarities. In addition, when a design is represented by a functional model using a function-flow format, many measures are available to determine functional similarity. In most current function-based design-by-analogy systems, the functions are represented as vectors and measures like cosine similarity are used to retrieve analogous designs. However, it is hypothesized that changing the similarity measure can significantly change the examples that are retrieved. In this paper, several similarity measures are empirically tested across a set of functional models of energy harvesting products. In addition, the paper explores representing the functional models as networks to find functionally similar designs using graph similarity measures. Surprisingly, the types of designs that are considered similar by vector-based and one of the graph similarity measures are found to vary significantly. Even among a set of functional models that share known similar technology, the different measures find inconsistent degrees of similarity — some measures find the set of models to be very similar and some find them to be very dissimilar. The findings have implications on the choice of similarity metric and its effect on finding analogous designs that, in this case, have similar pairs of functions and flows in their functional models. Since literature has shown that the types of designs presented can impact their effectiveness in aiding the design process, this work intends to spur further consideration of the impact of using different similarity measures when assessing design similarity computationally.