An interesting thing about the additive rule is that it can accommodate non-compensatory cut-off rules quite easily if the utilities are made extreme enough. As a simple example, if all the utilities are in the range of -3 to +3, but the respondent hates brand x... if brand x is given a utility of -100, then nothing can be done to add up utilities across the other attributes to compensate for the fact that the presence of brand x kills any likelihood that this respondent will pick a product with brand x.
So, you can see that if some utilities (representing "must-have" or "unacceptable" levels) are scaled extreme enough, the additive rule can correctly reflect non-compensatory decision-making.
While ACBC indeed will compute quite extreme good or bad utilities on levels that are must-haves or must-avoids (using HB estimation), we are reluctant to make the utilities wholly non-compensatory in nature. Too much experience and evidence over the years with human respondents suggests that many people will indicate that a level is a must-avoid or a must-have, when in reality their decision rules are not quite so fixed. So, the HB estimation process does some smoothing to the population means, even for extreme utilities, making the resulting extreme levels less extreme than they would have been if we take those cutoffs as absolutes, leaving open the possibility of trading into these alternatives if another really good features are present within the product concept.
So, in short, you may indeed use the additive logit rule (or first choice rules) in simulations based on ACBC questionnaires. It works out well.