The objective of this work is to explore the perceived visual and functional characteristics of computer generated sketches, compared to human created sketches. In addition, this work explores the possible biases that humans may have towards the perceived functionality of computer generated sketches. Recent advancements in deep generative design methods have allowed designers to implement computational tools to automatically generate large pools of new design ideas. However, if computational tools are to co-create ideas and solutions alongside designers, their ability to generate not only novel but also functional ideas, needs to be explored. Moreover, since decision-makers need to select those creative ideas for further development to ensure innovation, their possible biases towards computer generated ideas need to be explored. In this study, 619 human participants were recruited to analyze the perceived visual and functional characteristics of 50 human created 2D sketches, and 50 2D sketches generated by a deep learning generative model (i.e., computer generated). The results indicate that participants perceived the computer generated sketches as more functional than the human generated sketches. This perceived functionality was not biased by the presence of labels that explicitly presented the sketches as either human or computer generated. Moreover, the results reveal that participants were not able to classify the 2D sketches as human or computer generated with accuracies greater than random chance. The results provide evidence that supports the capabilities of deep learning generative design tools and their potential to assist designers in creative tasks such as ideation.