A common function in networking is to find “the best" match between a packet’s IP header and a list of matching rules, and to take some action based on the rule which is matched. This approach determines whether a packet transits a firewall or router and which interface is chosen for egress when it does, and whether a Network Address Translation transformation is applied. Considerable past research has optimized data structures and algorithms for rules-matching, under the operating assumption that with every specific application the best match is sought for a single IP flow, with a specified protocol, and specified source and destination IP and port numbers. This paper is motivated by a different scenario, in which we seek the simultaneous determination of the best matches for a bundle of flows. The flows are closely related as the bundle is a “contiguous" subset of the IP header space, meaning each flow draws in each dimension from the same range as other flows do in that same dimension. This specific problem arises in the design of tools that analyze the connectivity of networks. We consider here two algorithms for approaching this problem, which share the characteristic of generalizing the simulation of how devices typically classify a given flow. We study the behavior of these algorithms empirically, and find that the amortized cost of identifying the best matching rule in an ACL is typically measured in (at most) 10’s of micro-seconds on an ordinary laptop computer.