As demonstrated during the COVID-19 pandemic, non-pharmaceutical interventions, such as case isolation, are an important element of pandemic response. The overall impact of case isolation on epidemic dynamics depends on a number of factors, including the timing of isolation relative to the onset of contagiousness for each individual instructed to isolate by public health authorities. While there is an extensive literature examining the importance of minimising the delay from exposure to direction to isolate in determining the impact of case isolation policy, less is known about how underlying epidemic dynamics may also contribute to that impact. Empirical observation and modelling studies have shown that, as an epidemic progresses, the distribution of viral loads among cases changes systematically. In principle, this may allow for more targeted and efficient isolation strategies to be implemented. Here, we describe a multi-scale agent-based model developed to investigate how isolation strategies that account for cases viral loads could be incorporated into policy. We compare the impact and efficiency of isolation strategies in which all cases, regardless of their viral load, are required to isolate to strategies in which some cases may be exempt from isolation. Our findings show that, following the epidemic peak, the vast majority of cases identified with a low viral load are in the declining phase of their infection and so contribute less to overall contagiousness. This observation prompts the question about the potential public health value of discontinuing isolation for such individuals. Our numerical investigation of this 'adaptive' strategy shows that exempting individuals with low viral loads from isolation following the epidemic peak leads to a modest increase in new infections. Surprisingly, it also leads to a in efficiency, as measured by the average number of infections averted per isolated case. Our findings therefore suggest caution in adopting such flexible or adaptive isolation policies. Our multi-scale modelling framework is sufficiently flexible to enable extensive numerical evaluation of more complex isolation strategies that incorporate more disease-specific biological and epidemiological features, supporting the development and evaluation of future public health pandemic response plans.