Decision support systems (DSSs) are being woven into human workflows from aviation to medicine. In this study, we examine decision quality and visual information foraging for DSSs with different known reliability levels. Thirty-six participants completed a financial fraud detection task, first unsupported and then supported by a DSS which highlighted important information sources. Participants were randomly allocated to four cohorts, being informed that the system's reliability was 100%, 90%, 80% or undisclosed. Results showed that only a DSS known to be 100% reliable resulted in participants systematically following its suggestions, increasing the percentage of correct classifications to a median of 100% while halving both, decision time and number of visually attended information sources. In all other conditions, the DSS had no effect on most visual sampling metrics, while decision quality of the human-DSS team was below the reliability level of the DSS. Knowledge of an even slightly unreliable system hence had a profound impact on joint decision making, with participants trusting their significantly worse performance more than the DSSs suggestions.