Classification problem with many images per instance
This might be a hack, but, have you considered repeating some images to make every instance contain a fix number of images? Let's say you have images A, B, C, D, E, F, G, H, and I. Your dataset might look like this:


A , B
C
D, E, F
G
H, I


Make a function that will randomly select one of the image in an instance and then use that as placeholder. From our example, we now modify our dataset into:


A, B, A
C, C, C
D, E, F
G, G, G
H, I, I


Then, from here, you can use a convolutional neural network with 3 inputs. Here is an example of a CNN with multiple inputs: Multi-Input Convolutional Neural Network for Flower Grading - images of same flowers taken at different angles.

Also, you can shuffle the images in each instance to produce more training samples.